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Technical Lead, Machine Learning
Company A1 is building a proactive AI smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Role As Technical Lead, Machine Learning, you own the execution layer of A1’s intelligence. You translate research direction into reliable, scalable, production-grade ML systems. This role sits at the intersection of research, infrastructure, and product. You are responsible for making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. Make pragmatic trade-offs and ship improvements quickly, learning from real usage. Work under real production constraints: latency, cost, reliability, and safety Outcomes Research and models reliably translate into production-ready solutions with clear performance and quality targets. ML pipelines, training loops, and inference systems are stable, efficient, and maintainable. Production issues are detected, debugged, and resolved quickly, minimizing user impact. Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction. Iterations on models and systems are measurable, safe, and improve user experience over time. Tech Stack Python PyTorch / JAX GPU-based training and inference system Ideal Experience You have built or shipped real ML systems used by people, not just demos. You are comfortable working with large models and understanding their failure modes. You write strong, production-grade code and care about system correctness. You are self-directed, pragmatic, and take full ownership of outcomes. You communicate clearly and collaborate well in small, high-trust teams. How We Work The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product Interview process If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews. Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
Engineering Manager, Machine Learning
About Sentry Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. About the role AI and machine learning are reshaping how developers debug, monitor, and ship software, and Sentry is uniquely positioned to lead that shift. We sit on a novel and massive dataset of real production errors, spans, and logs from tens of thousands of engineering organizations — the kind of signal that makes ML genuinely useful, whether it's a clustering model that groups related issues, a ranking system that surfaces the right alert at the right time, or an agent that proposes a fix. We're looking for an Engineering Manager to lead and grow our Machine Learning Engineering team. This team owns the full spectrum of ML at Sentry: classical techniques like clustering, ranking, anomaly detection, and embeddings that quietly power core product surfaces today, alongside the LLM-based and agentic systems shaping where the product is headed. You'll partner closely with product, design, and engineering leaders to decide where ML belongs in our products, what kind of ML actually fits the problem, and how we translate that work into experiences millions of developers rely on every day. In this role you will Set technical direction across the team's full ML surface area — from classical models for clustering, ranking, and anomaly detection to LLM-based and agentic systems — and make sharp calls about which approach fits each problem Define how the team evaluates and monitors ML systems in production, from offline metrics to online experimentation to model and agent observability Stay hands-on enough to review code and model designs, contribute to architecture discussions, and unblock engineers on complex ML problems Define team roadmap and deliverables, scope work, allocate resources, and keep execution on track against ambitious goals Partner with product managers, designers, and engineering leaders across Sentry to identify the highest-impact opportunities for ML in our products Foster career growth for the engineers on your team, and recruit exceptional ML talent as the team scales You'll love this role if you Have a strong ML engineering background that you use to inform and validate the team's technical decisions, and you're comfortable staying close to the code Are excited to shape how AI and developer tools come together, and want to define how engineers and coding agents collaborate to find and fix problems Are driven by impact and want to lead a team working on high-stakes, high-visibility projects Love building things from the ground up — the ML team is still in its early chapters, and there's plenty of greenfield to shape Thrive in cross-functional environments and enjoy collaborating across product, design, and engineering to ship great work Care deeply about growing the engineers around you and finding opportunities for them to do the best work of their careers Qualifications 8+ years of professional engineering experience, with significant time spent building and shipping machine learning systems in production 3+ years of engineering management experience, ideally leading ML, AI, or data-focused teams Familiarity with deploying and operating ML models at scale, including evaluation, monitoring, and iteration in production Strong judgment in ambiguous, fast-moving environments Excellent written and verbal communication; comfortable working across product, research, and engineering A research background in machine learning, statistics, or a related field (MS, PhD, or equivalent research experience) is a plus but not a requirement Not sure if you meet 100% of the qualifications? We encourage you to apply anyway. We're interested in people who are excited about this opportunity and eager to grow. The base salary range (or hourly wage range, if applicable) that Sentry reasonably expects to pay for this position is $220,000 to $280,000. A successful candidate's actual base salary (or hourly wage) amount will be determined by a variety of relevant factors including, without limitation, the candidate's work location, education, work and other relevant experience, skills, and job-related knowledge. A successful candidate will be eligible to participate in Sentry's employee benefit plans/programs applicable to the candidate's position (including incentive compensation, equity grants, paid time off, and group health insurance coverage). See Sentry Benefits for more details about the Company's benefit plans/programs. Equal Opportunity at Sentry Sentry is committed to providing equal employment opportunities to its employees and candidates for employment regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other legally-protected characteristic. This commitment includes the provision of reasonable accommodations to employees and candidates for employment with physical or mental disabilities who require such accommodations in order to (a) perform the essential functions of their jobs, or (b) seek employment with Sentry. We strive to build a diverse team, with an inclusive culture where every teammate can thrive. Sentry is an open-source company because we believe that everyone, everywhere, should have the ability and tools to make great software. Software should be accessible. That starts with making our industry accessible. If you need assistance or an accommodation due to a disability, you may contact us at accommodations@sentry.io . Want to learn more about how Sentry handles applicant data? Get the details in our Applicant Privacy Policy .
Staff Machine Learning Engineer, AI
About Sentry Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. About the role As a Staff Machine Learning Engineer on Sentry’s AI/ML team, you’ll be directly responsible for developing the models and agents used to make our product smarter and more capable. This role is crucial; you will be at the forefront of integrating AI and machine learning into our core products, from issue triage and resolution to predictive analytics for application performance monitoring. Your work will help companies around the globe gain actionable insights into their software, enabling them to build better products, faster. In this role you will Build state-of-the-art agentic AI systems to triage, debug, and solve real production issues Leverage Sentry’s novel (and massive) dataset of errors, spans, and profiles Own the development of major initiatives in the AI/ML space You'll love this job if you Are driven by impact and enjoy working on high-stakes, high-visibility projects Enjoy building things. You will have the opportunity to join the AI/ML team as one of its foundational members Thrive in cross-functional teams and enjoy building features alongside developers and product teams Qualifications Minimum 4+ years of professional experience with a MS/PhD degree in computer science, machine learning, or a related field Minimum 6+ years of professional experience with Bachelor’s degree in computer science, machine learning, or a related field Demonstrated expertise building production-grade agentic systems and tools You are comfortable writing production quality code (we use Python) Expertise with deep learning frameworks (we use PyTorch) Familiarity in deploying machine learning models at scale in production environments Experience in writing technical documentation, mentoring, and presenting to technical audiences Proven track record of owning a system, feature, or component, leading or collaborating with multiple engineers and teams The base salary range (or hourly wage range, if applicable) that Sentry reasonably expects to pay for this position is $240,000 to $300,000. A successful candidate’s actual base salary (or hourly wage) amount will be determined by a variety of relevant factors including, without limitation, the candidate’s work location, education, work and other relevant experience, skills, and job-related knowledge. A successful candidate will be eligible to participate in Sentry’s employee benefit plans/programs applicable to the candidate’s position (including incentive compensation, equity grants, paid time off, and group health insurance coverage). See Sentry Benefits for more details about the Company’s benefit plans/programs. Equal Opportunity at Sentry Sentry is committed to providing equal employment opportunities to its employees and candidates for employment regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other legally-protected characteristic. This commitment includes the provision of reasonable accommodations to employees and candidates for employment with physical or mental disabilities who require such accommodations in order to (a) perform the essential functions of their jobs, or (b) seek employment with Sentry. We strive to build a diverse team, with an inclusive culture where every teammate can thrive. Sentry is an open-source company because we believe that everyone, everywhere, should have the ability and tools to make great software. Software should be accessible. That starts with making our industry accessible. If you need assistance or an accommodation due to a disability, you may contact us at accommodations@sentry.io . Want to learn more about how Sentry handles applicant data? Get the details in our Applicant Privacy Policy .
Software Engineer, Character Platform & Tools
About Sesame Sesame believes in a future where computers are lifelike - with the ability to see, hear, and collaborate with us in ways that feel natural and human. With this vision, we're designing a new kind of computer, focused on making voice agents part of our daily lives. Our team brings together founders from Oculus and Ubiquity6, alongside proven leaders from Meta, Google, and Apple, with deep expertise spanning hardware and software. Join us in shaping a future where computers truly come alive. About the Role The best platforms don't just support a workflow — they transform who can participate in it. At Sesame, we're building lifelike, compelling characters. That quality doesn't come from models alone. It comes from tight collaboration between engineers, ML researchers, and the creative professionals who define what a believable character is. This is a full-stack engineering role focused on the internal tooling and platforms that power our character research pipeline. We're looking for engineers who are excited to build the systems that enable and accelerate that collaboration. You'll architect and develop the internal platforms that take our team from raw data to fully-realized characters — evaluating quality and iterating cross-functionally through a React-based frontend atop a Python/FastAPI backend, backed by cloud ML infrastructure. The goal isn't just to support what we're doing today; it's to build a platform where our creative and research teams can experiment, iterate, and push the frontier of what's possible in character AI. Responsibilities: Design and build internal tools that let engineers, researchers, and creative professionals experiment with and assemble configurations of models — importing datasets, running training jobs, evaluating outputs — without needing to write code for every workflow Build rich evaluation interfaces that surface model quality in ways that are readable and actionable for everyone, not just engineers Collaborate closely with ML researchers and creative teams to deeply understand their workflows, then find the automation opportunities that accelerate their work Take responsibility for the reliability and evolution of the tooling platform: monitoring, iterating on user feedback, and raising the ceiling of what's possible as the team's needs grow Contribute to ML infrastructure where it connects to tooling — you'll understand the pipeline well enough to know what to expose, what to abstract, and what to automate Required Qualifications: Demonstrated 4+ years experience building and shipping production full-stack systems, with a strong sense of UI/UX for tools designed to be used by experts in other fields Experience building internal platforms or tooling, not just features — you think about the system, not just the ticket Proficiency with modern web stacks (we use React/Next.js and Python/FastAPI) Experience with cloud infrastructure (we're on GCP/Kubernetes) A strong sense of reliability and knowing when to build vs. borrow Curiosity about the domain: you don't need to be an ML engineer, but you should want to understand what you're building tools for Comfort working across disciplines — researchers, creative professionals, and operators — and translating between very different ways of thinking Sesame is committed to a workplace where everyone feels valued, respected, and empowered. We welcome all qualified applicants, embracing diversity in race, gender, identity, orientation, ability, and more. We provide reasonable accommodations for applicants with disabilities. Contact careers@sesame.com for assistance. Full-time Employee Benefits: 401 (k) max employer match: 3.5% of compensation 100% employer-paid health, vision, and dental benefits for you and your dependents Unlimited PTO and sick time Flexible spending account with employer matching up to $1,650/year (medical FSA) Guardian Employee Assistance Program (EAP) Opportunity to share in the company's success with competitive stock options Benefits do not apply to contingent/contract workers.
Lead Machine Learning Engineer
Shape the Future of AI & Data with Us At Datatonic, we are Google Cloud's premier partner in AI, driving transformation for world-class businesses. We push the boundaries of technology with expertise in machine learning, data engineering, and analytics on Google Cloud Platform. By partnering with us, clients future-proof their operations, unlock actionable insights, and stay ahead of the curve in a rapidly evolving world. Your Mission As a Lead Machine Learning Engineer , you will be the technical authority for our most ambitious client projects. You will set the technical vision, guide teams of talented engineers, and translate complex business challenges into cutting-edge AI solutions on Google Cloud. You'll be an advocate of high-quality engineering and best-practice in production software as well as rapid prototypes. This is a hands-on leadership role where you will not only architect solutions but also actively lead client discussions and oversee project delivery from start to finish. Your responsibilities will involve building trusted relationships with prospects, finding creative ways to use machine learning to solve problems, scoping projects, and overseeing the delivery of these engagements. To be successful, you will need strong ML & Data Science fundamentals and will know the right tools and approach for each ML use case. You'll be comfortable with model optimization and deployment tools and practices. Furthermore, you'll also need excellent communication and consulting skills, with the desire to meet real business needs and deliver innovative solutions using AI & Cloud. What You’ll Do Drive Technical Strategy from Pre-Sales to Delivery: Act as the lead technical authority in high-stakes engagements. You will partner with our commercial team to architect winning solutions, and then lead the delivery of enterprise-grade systems—such as GenAI agents for financial institutions, real-time recommendation engines for global retailers, or predictive maintenance models for the manufacturing sector. Architect & Implement Production-Grade Solutions : Own the complete technical lifecycle for your projects. You will design end-to-end ML architectures on GCP, implement robust MLOps pipelines using Infrastructure-as-Code (Terraform), and ensure all solutions—from sophisticated multi-agent systems to classical models—are optimized for performance, scalability, and security. Shape Our Technical Standards: Collaborate directly with the Head of Delivery to define the technical DNA of our ML practice, evolving the best practices, architectural patterns, and standards that ensure excellence across the company. Lead Strategic Internal Initiatives: Spearhead the development of internal accelerators and reusable frameworks that enhance our delivery capabilities and solidify our position as industry leaders. Cultivate Engineering Talent: Formally mentor and coach our junior and mid-level engineers, elevating the team's collective skill set through rigorous code reviews, technical guidance, and career development. What You’ll Bring Experience: You have 7+ years of professional experience in machine learning and software engineering, with at least 2 years in a formal or informal leadership capacity (e.g., tech lead, project lead, or senior mentor). Cloud & Systems Architecture Expertise : You possess a proven ability to architect and deploy scalable, production-grade ML solutions on a major cloud platform (GCP is a significant asset). This includes hands-on experience with Infrastructure-as-Code tools (e.g., Terraform) and designing for distributed computing. Expert-Level Engineering Craftsmanship: You have deep, hands-on expertise in Python for backend ML systems and a mastery of software engineering best practices (e.g., clean architecture, robust testing, CI/CD). You can fluently design and build REST APIs (e.g., using Flask/FastAPI) and are proficient in SQL for complex data manipulation. Consultative Communication & Mentorship : You have an exceptional ability to communicate complex technical concepts to diverse audiences, from C-level stakeholders to junior engineers. You excel at leading technical discussions, presenting solutions, and mentoring teammates to elevate their skills. Bonus Points If You Have: Prior experience in a client-facing or consulting role. Professional Google Cloud certifications (e.g., Professional Machine Learning Engineer). Deep experience with the broader MLOps ecosystem (e.g., Kubeflow, Vertex AI Pipelines, MLflow). Experience building interactive demos for ML models (e.g., using Streamlit, Gradio). What’s in It for You? We believe in empowering our team to thrive, with benefits including: 20 days of paid vacation per calendar year Public Holidays for your Province of Residence 5 Wellness days (sickness, personal time, mental health) 5 Lifestyle days (religious events, volunteer day, sick day) Matching Group Retirement Savings Plan after 3 months Competitive Group Insurance plan on Day 1 - individual premium paid 100%! Virtual Medicine and Family Assistance Program - 100% employer-paid! Home office budget - We are 100% remote! CAD $70/month for internet/phone expenses Company-supplied MacBook Pro or Air CAD $400/year for books, relevant app subscriptions or an e-reader. Opportunities for paid certifications Opportunities for professional and personal learning through Udemy Business Regular company off-sites and meetups Salary: CAD 175,000 - 200,000. Why Datatonic? Datatonic is a UK-based company with an Americas division located in Canada. The Canadian team operates remotely, with members distributed across North and South America. This role is open to candidates located anywhere in Canada. Join us to work alongside AI enthusiasts and data experts who are shaping tomorrow. At Datatonic, innovation isn’t just encouraged - it’s embedded in everything we do. If you’re ready to inspire change and deliver value at the forefront of data and AI, we’d love to hear from you! Are you ready to make an impact? Apply now and take your career to the next level.
Machine Learning Engineer: Evaluation
Join the team bringing advanced autonomy to the built world At Bedrock, we’re moving AI out of the lab and into the real world. Our team is composed of industry veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we’re deploying autonomous systems on heavy construction machinery across the country, accelerating project schedules of billion-dollar infrastructure projects and improving safety on job sites. Backed by $350M in funding, we’re working quickly to close the gap between America's surging demand for housing, data centers, manufacturing hubs, and the construction industry's growing labor shortage. This is where algorithms meet steel-toed boots. You’ll collaborate with construction veterans and world-class engineers to solve physical-world problems that simulations can’t touch. If you're ready to apply cutting-edge technology to solve meaningful problems alongside a talented team—we'd love to have you join us. Machine Learning Engineer: Evaluation Bedrock is bringing autonomy to the construction industry! We’re a group of veterans from the autonomous vehicle industry who are passionate about bringing the benefits of automation to areas in the construction industry currently underserved by the market. We’re looking for a highly motivated engineer with experience evaluating complex ML systems deployed in the real world. Your Mission: Translate the infinite nuance of the built world into actionable, AI-native evaluations that accelerate Bedrock Operator adoption. The ideal candidate has hands-on experience in building evaluation systems and designing and executing statistical tests to gauge performance deltas between system iterations. More importantly, you’ve iterated on complex ML systems run in production environments, and you understand the complexities that come with it. What you’ll do: Design and maintain eval systems: Build pipelines for measuring system performance – across open loop and closed loop simulation, hardware in the loop systems, and field data from Bedrock Operator equipped machinery. Excite other teams to gain insights earlier in the development cycle through streamlined workflows. Develop metrics: Connect product goals and system behavior - by bridging real-world specification to measurable indicators from logged data. Empower confident decision making from parameter tuning to program planning by slicing through the noise and delivering objective insights. Classify data sources for training and testing: Implement infrastructure and classifiers - to self-annotate data and allow creation of datasets for a variety of training and evaluation use cases. Leverage models to source rich annotations for massive datasets to accelerate model iteration. Predict system performance: Model metrics and interpret results - from various sources ranging from raw sensor data to key leading indicators. Determine whether new construction sites pose hidden challenges and drive business decisions about deployment readiness. What we’re looking for: Engineers who are currently Senior or Staff level with 5+ years of professional software engineering, data science, or research experience 2+ years of professional experience analyzing modern ML or robotics system performance on real-world problems Proficiency in Python and a data warehouse query language and comfort with development on infrastructure within parallelized cloud-based frameworks Strong statistical analysis skills (e.g. classification, model fit bias determination, hypothesis testing, and uncertainty quantification) Experience working with large datasets ***Bonus points: We’re especially interested in engineers who have applied statistical backgrounds to ML research or real-world robotics applications. Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you. Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.
Machine Learning Engineer: Perception
Join the team bringing advanced autonomy to the built world At Bedrock, we’re moving AI out of the lab and into the real world. Our team is composed of industry veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we’re deploying autonomous systems on heavy construction machinery across the country, accelerating project schedules of billion-dollar infrastructure projects and improving safety on job sites. Backed by $350M in funding, we’re working quickly to close the gap between America's surging demand for housing, data centers, manufacturing hubs, and the construction industry's growing labor shortage. This is where algorithms meet steel-toed boots. You’ll collaborate with construction veterans and world-class engineers to solve physical-world problems that simulations can’t touch. If you're ready to apply cutting-edge technology to solve meaningful problems alongside a talented team—we'd love to have you join us. Machine Learning Engineer: Perception Bedrock is bringing autonomy to the construction industry! We’re a group of veterans from the autonomous vehicle industry who are passionate about bringing the benefits of automation to areas in the construction industry currently underserved by the market. We are looking for engineers with expertise in shipping production 3D perception systems at scale. Successful candidates have architected systems, trained models from scratch, understand the full stack (clustering, detection, classification, and tracking), and have shipped at scale. We use both computer vision and LIDAR-based approaches, so knowledge of either or both is key. Models are just part of the system: you understand data and have good intuition about why models fail. You know how to evaluate corner cases, manage or build data pipelines, use autolabels (or not), and have a strong understanding of statistical properties of these systems. What You’ll Do: Design Early Fusion Architectures: Develop and train state-of-the-art models (e.g., BEV-based transformers) that fuse raw Lidar and Camera data to solve for object detection and semantic segmentation. Tackle "Messy" Physics: Build perception systems robust enough to handle dynamic occlusion (seeing the robot’s own arm/bucket), particulates (dust, snow, rain), and high-vibration conditions. Deploy to the Edge: Optimize models for inference on embedded hardware. You will debug system-level issues, such as sensor calibration drift and latency bottlenecks. Collaborating with other teams to create state-of-the-art representations for downstream use cases. What we're looking for: Production ML Experience: 3+ years of experience taking deep learning models from research to real-world production using PyTorch, Tensorflow, or JAX. 3D Geometry & Calibration: You have a deep understanding of SE(3) transformations, homogeneous coordinates, and intrinsic/extrinsic sensor calibration. You understand the math required to project a 3D Lidar point onto a 2D image pixel accurately. Early Fusion Expertise: Practical experience with architectures that fuse modalities at the feature level (e.g., BEVFusion, TransFuser, PointPainting) rather than just fusing final bounding boxes. SOTA Object Detection experience with modern transformer-based architectures (DETR, PETR, etc…) including similar temporal models (PETRv2, StreamPETR, …) Systems Fluency: You are an expert in Python, but you are also comfortable reading and writing systems code in C++ or Rust. You understand memory management and real-time constraints. Data Intuition: You understand that in robotics, better data alignment often beats a bigger model. You are willing to dig into the data infrastructure to ensure ground truth quality. Ways to stand out: Bonus: Voxel/Occupancy Experience: Experience working with occupancy grids, NeRFs, or voxel-based representations for terrain mapping. Bonus: Top-Tier Research: Published work in conferences such as ICRA, IROS, CVPR, ECCV, ICCV, CoRL, or RSS Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.
Machine Learning Engineer
About Casap Casap is a Series A startup that has raised over $30M from Emergence, Lightspeed, and Primary Ventures. Based in San Francisco, the company was founded by product leaders from Robinhood and Chime. We are on a mission to change the way banks operate by automating disputes and fighting friendly fraud. People love what we’ve built, from everyday users to the biggest names in finance. Casap is looking for a talented Machine Learning Engineer in San Francisco excited to supercharge how banks operate with a rapidly scaling product at the forefront of automation and Artificial Intelligence. Responsibilities Develop and implement machine learning models to evaluate disputes and chargebacks and likelihood of fraud Create and manage an orchestration layer for multiple models, enabling automated and supervised decision-making processes Collaborate with stakeholders to leverage valuable data from partners and customers, ensuring a continuously learning experience and first-class user experience Qualifications 3+ years of experience designing and deploying ML models in a high-scale production environment. Strong knowledge of machine learning algorithms and data analysis techniques. Enthusiastic about building ML infrastructure from scratch in a nascent environment. Ability to think like a product engineer, balancing technical innovation with practical application and user needs. Strong proficiency in programming languages like Python, R, or similar, and experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn. Excellent problem-solving skills and the ability to work independently and in a collaborative team environment.
Machine Learning Engineer
About Reducto Reducto is the agentic document platform for leading AI teams who demand enterprise performance at scale. We provide a comprehensive toolkit for working with documents the way a human would, combining custom in-house and leading frontier models to power efficient and accurate document workflows. We’ve grown rapidly, increasing revenue 8x year over year and partnering with hundreds of companies, from leading AI teams like Harvey, Vanta, and Scale, to enterprise customers across FAANG and top trading firms. Reducto has raised over $100M from world-class investors including a16z, Benchmark, and First Round Capital. We would love to meet you if you: Philosophy: You are your own worst critic. You have a high bar for quality and don’t rest until the job is done right—no settling for 90%. We want someone who ships fast, with high agency, and who doesn't just voice problems but actively jumps in to fix them. Experience: You have 2+ years of experience with training, fine tuning, and evaluating ML models used in production systems Language/Skills: You’re exceptional at Python or similar, and are well versed with both traditional computer vision and VLMs Tools: Build your own tools as needed—like a quick Streamlit app to test hypotheses or create a dataset. Approach: A quantitative approach to building products. Ability to debug, experiment, and iterate fast. You should be comfortable getting hands-on with the full development lifecycle, from ideation to shipping to users. The core work will include: Training and deploying new state of the art models for parsing and interpreting unstructured data Experimenting with novel techniques to improve LLM accuracy Build data pipelines, evaluate model performance, and integrate models into the product Working directly with the founders and customers to shape the product direction and engineering strategy Bonus points if you: Have prior experience founding a company or building products at early stages Are ambitious and driven, and care a lot about doing great work with great people Keep up with the latest developments in ML/AI This is an in person role at our office in SF. We’re an early stage company which means that the role requires working hard and moving quickly. Please only apply if that excites you. About Reducto Nearly 80% of enterprise data is in unstructured formats like PDFs PDFs are the status quo for enterprise knowledge in nearly every industry. Insurance claims, financial statements, invoices, and health records are all stored in a structure that’s simply impractical for use in digital workflows. This isn’t an inconvenience—it’s a critical bottleneck that leads to dozens of wasted hours every week . Traditional approaches fail at reliably extracting information in complex PDFs OCR and even more sophisticated ML approaches work for simple text documents but are unreliable for anything more complex. Text from different columns are jumbled together, figures are ignored, and tables are a nightmare to get right. Overcoming this usually requires a large engineering effort dedicated to building specialized pipelines for every document type you work with. Reducto breaks document layouts into subsections and then contextually parses each depending on the type of content. This is made possible by a combination of vision models, LLMs, and a suite of heuristics we built over time. Put simply, we can help you: Accurately extract text and tables even with nonstandard layouts Automatically convert graphs to tabular data and summarize images in documents Extract important fields from complex forms with simple, natural language instructions Build powerful retrieval pipelines using Reducto’s document metadata Intelligently chunk information using the document’s layout data Benefits at Reducto At Reducto, we’re invested in the well-being and growth of our team. Here’s what we currently offer: Unlimited PTO: We believe great work requires recharging. Lunch: Receive a free lunch to eat with your teammates daily at the office Reimbursed Transportation: Provide us with your receipts and we’ll take care of the costs Insurance : Generous health insurance covering medical, dental, and vision. Health and Wellness Budget: We provide up to $150/mo reimbursement for health and wellness spending, such as gym memberships, fitness classes, or similar. Parental Leave: Work with us to build a leave schedule that works for you and your family Reducto is an Equal Opportunity Employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, physical and mental disability, genetic information, marital status, sexual orientation, gender identity/assignment, citizenship, pregnancy or maternity, protected veteran status, or any other status prohibited by applicable national, federal, state or local law.
Machine Learning Infra Engineer
About Reducto Reducto is the agentic document platform for leading AI teams who demand enterprise performance at scale. We provide a comprehensive toolkit for working with documents the way a human would, combining custom in-house and leading frontier models to power efficient and accurate document workflows. We’ve grown rapidly, increasing revenue 8x year over year and partnering with hundreds of companies, from leading AI teams like Harvey, Vanta, and Scale, to enterprise customers across FAANG and top trading firms. Reducto has raised over $100M from world-class investors including a16z, Benchmark, and First Round Capital. The Opportunity As an ML Infra Engineer , you’ll play a key role in building the inference and training frameworks that make it possible to deliver results at scale. You’ll collaborate closely with our ML and Platform teams to scale training across nodes, develop faster and more efficient serving, and create observability across the stack. This is a high-impact role where you’ll help define what high performance ML training and inference look like at Reducto. What You’ll Do Build, and maintain our training and inference stack with an emphasis for fast iteration on training + flexibility for exploring new methods and high performance in inference. Develop benchmarks for both sets of stacks to identify bottlenecks. Explore SOTA advances in training and inference and work to apply them. Design systems for scaling model training across multi-node, multi-GPU environments with strong reliability and observability. Scale distributed training and inference workloads across large GPU clusters while improving utilization, reliability, and cost efficiency. Build the tooling, abstractions, and observability that help ML engineers move faster from experiment to production. You’ll Thrive Here If You: Hold yourself to a high bar for quality and precision. Enjoy solving complex problems and building from first principles. Have strong Python skills + a background in systems engineering. Are comfortable with Kubernetes and distributed training frameworks. Love getting your hands dirty with real-world implementation challenges. Operate well in fast-changing, high-growth environments. Collaborate effectively across technical and non-technical teams. Take full ownership from strategy through execution. Have 3+ years of experience. Bonus points if you: Have experience at an early-stage or high-growth startup. Have developed in open source training/inference stacks in a meaningful way. Are excited to set up distributed inference across 100s-1000s of GPUs. Care deeply about combining technical excellence with business impact. This is an in person role at our office in SF. We’re an early stage company which means that the role requires working hard and moving quickly. Please only apply if that excites you. More about Reducto Nearly 80% of enterprise data is in unstructured formats like PDFs PDFs are the status quo for enterprise knowledge in nearly every industry. Insurance claims, financial statements, invoices, and health records are all stored in a structure that’s simply impractical for use in digital workflows. This isn’t an inconvenience—it’s a critical bottleneck that leads to dozens of wasted hours every week . Traditional approaches fail at reliably extracting information in complex PDFs OCR and even more sophisticated ML approaches work for simple text documents but are unreliable for anything more complex. Text from different columns are jumbled together, figures are ignored, and tables are a nightmare to get right. Overcoming this usually requires a large engineering effort dedicated to building specialized pipelines for every document type you work with. Reducto breaks document layouts into subsections and then contextually parses each depending on the type of content. This is made possible by a combination of vision models, LLMs, and a suite of heuristics we built over time. Put simply, we can help you: Accurately extract text and tables even with nonstandard layouts Automatically convert graphs to tabular data and summarize images in documents Extract important fields from complex forms with simple, natural language instructions Build powerful retrieval pipelines using Reducto’s document metadata Intelligently chunk information using the document’s layout data Benefits at Reducto At Reducto, we’re invested in the well-being and growth of our team. Here’s what we currently offer: Unlimited PTO: We believe great work requires recharging. Lunch: Receive a free lunch to eat with your teammates daily at the office Reimbursed Transportation: Provide us with your receipts and we’ll take care of the costs Insurance : Generous health insurance covering medical, dental, and vision. Health and Wellness Budget: We provide up to $150/mo reimbursement for health and wellness spending, such as gym memberships, fitness classes, or similar. Parental Leave: Work with us to build a leave schedule that works for you and your family Reducto is an Equal Opportunity Employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, physical and mental disability, genetic information, marital status, sexual orientation, gender identity/assignment, citizenship, pregnancy or maternity, protected veteran status, or any other status prohibited by applicable national, federal, state or local law.
Systems Engineer - Machine Learning
General Robotics is an AI research and deployment company building a platform for general robot intelligence. Our mission is to enable rapid, robust, and safe deployment of general intelligence for autonomous systems and robotics. We aspire to become the starting point for AI-powered autonomous systems across a diverse set of scenarios. Position Overview We are seeking an ML Engineer to join our team in Redmond, WA. We build and optimize the platform that serves ML models to robots in real-time — from perception and planning to foundation models — with a focus on low latency, high throughput, reliable and robust robot-to-cloud communication. We are looking for strong candidates who have a background in ML infrastructure and model serving, with experience in areas like CUDA kernel programming; distributed serving frameworks; real-time streaming; and taking research models to production. By applying to this role, you will be considered for multiple teams, such as platform infrastructure, ML systems, and edge deployment. Responsibilities Integrate and productionize state-of-the-art ML models into our serving infrastructure, collaborating with research teams to bring new architectures from prototype to deployment.Contribute to infrastructure tooling that makes onboarding new models faster and more reliable. Develop and maintain low-latency, high-throughput pipelines for ML model inference across robotics workloads. Optimize GPU workloads and accelerate ML frameworks for real-time performance: data transfer, memory management, batching, serialization, and concurrent request handling. Qualifications Bachelor’s degree in Computer Science, Computer Engineering, or relevant technical field, or equivalent practical experience. 1+ years of experience in ML infrastructure, model serving, or backend systems engineering. Strong Python. Comfortable navigating unfamiliar research codebases and turning them into clean, production services. Familiarity with ML frameworks (PyTorch, JAX), containerized deployments (Docker, Kubernetes), and distributed serving frameworks (Ray, Triton, or similar). Familiarity with async Python, real-time communication protocols, and robotics systems is a plus. Familiarity with cloud platforms (AWS, GCP, Azure) and infrastructure-as-code tooling. Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment. Base Salary Range The anticipated base salary for this position is $155000-$200000. Your actual pay will be based on factors such as skills, experience, and location. In addition to base salary, this role is eligible for benefits including medical, 401K, and other health benefits. Work Authorization This role is open to candidates currently based in and authorized to work in the US. Equal Opportunity Employer General Robotics is an equal opportunity employer. We do not discriminate on the basis of any status protected by applicable law. Accommodations If you need a reasonable accommodation during the application or interview process, please contact: hr@generalrobotics.company
Machine Learning Engineer, Detection (NY)
Why Join Doppel Doppel is building the future of social engineering defense. Our AI-native platform uses agentic AI to protect executives, employees, customers, and brands from phishing, impersonation, fraud, and other AI-powered threats across digital channels. We help some of the world’s most recognized brands detect and dismantle attacker infrastructure while strengthening employee resilience through threat-informed training and simulation. By unifying Digital Risk Protection, Human Risk Management and Email Security, Doppel connects threats into a real-time intelligence graph to power faster disruption, smarter defense, and modern security awareness at scale. Backed by leading investors including Andreessen Horowitz and Bessemer Venture Partners, and trusted by leading enterprises, Doppel is a rapidly growing Series C startup building the future of social engineering defense. Our team combines deep cybersecurity expertise, operational rigor, and startup velocity to solve some of the internet’s most urgent trust and safety challenges. What We're Building We're building the AI-native social engineering defense platform. This means we're designing scalable systems that monitor billions of domains, social media accounts, apps, dark web forums, etc., and leverage AI agents to identify and neutralize digital threats. What We're Looking For We’re looking for a machine learning engineer to help build and scale the models and systems that power Doppel’s detection systems. Check out our blog post on how we set up our ML platform. As an MLE at Doppel, you will Design, train, and deploy models for both batch and real-time inference that identify malicious or infringing content across diverse data sources. Partner closely with the Detection and Infrastructure teams to ensure our ML systems scale with the volume of web data we ingest Work on problems that range from NLP and embeddings to similarity search, classification, and anomaly detection Collaborate directly with customers and internal stakeholders to translate real-world threats into production ML systems You may be a fit if you: Have experience building and deploying ML systems in production environments Are comfortable working with large-scale datasets and distributed data processing frameworks Understand the trade-offs between research-quality models and production-ready systems Are excited about solving real-world problems where the adversary is constantly evolving What We Offer 🚀 A mission-driven culture with low ego, high ownership, deep customer obsession, and exceptional talent density 🍽️ Free lunch and dinner in the office 🌴 Flexible PTO ✈️ Quarterly team offsites Join Doppel Doppel is the first platform built to dismantle digital deception at scale. We scan over 150 billion entities daily and deploy continuously adaptive AI SOC agents, paired with expert human analysts, to uncover and disrupt the infrastructure behind phishing, impersonation, and online fraud before attacks can spread. Our Threat Grid turns every customer signal into shared intelligence, making each disruption smarter, faster, and more effective. We’re not just another cybersecurity company. We’re defining the future of social engineering defense, where trust is protected, and deception becomes unprofitable. Backed by top-tier investors and trusted by some of the world’s most recognized brands, Doppel is growing fast. If you’re driven to solve real-world problems with bold technology, we’d love to meet you.
Senior Machine Learning Engineer
Special Notice: This position is NOT contingent upon awarding of a project or needing a funding source. This is full-time employment with webAI. About the Role: We are seeking a Senior Machine Learning Engineer to support our Public Sector initiatives focused on building and optimizing production ready AI systems for secure and distributed environments. You will be responsible for transforming prototype models into scalable, efficient, and reliable production systems that operate seamlessly across a spectrum of hardware from government cloud infrastructure to edge devices in restricted or disconnected environments. Responsibilities: Design, develop, and deploy agentic workflows to orchestrate multi-step reasoning, tool use, and decision-making across production systems. Productionize AI models from research prototypes into scalable, deployable systems used in real world applications. Engineer adaptive ML systems using LoRA, PEFT, and on-device inference strategies, leveraging PyTorch, TensorFlow, and Hugging Face Transformers for model development, fine-tuning, and optimization. Implement model optimization techniques such as quantization, pruning, distillation, and hardware specific acceleration. Build and maintain Retrieval Augmented Generation (RAG) pipelines, including vector database integration for contextual retrieval. Work with multi-modal AI systems across computer vision, audio, and natural language domains. Optimize model execution for distributed and resource constrained environments, ensuring reliability under variable connectivity conditions. Qualifications: Active US Security clearance 4+ years of experience in applied AI, ML engineering, or production AI systems. Deep proficiency in PyTorch, TensorFlow, or Hugging Face Transformers. Proven experience deploying AI models across cloud, edge, and mobile hardware environments. Expertise in model compression and optimization (quantization, pruning, distillation). Experience building RAG pipelines and integrating vector databases (e.g., Quadrant, ChromaDB, FAISS, Milvus, Pinecone). Familiarity with multi-modal models and synthetic data generation methods. Strong algorithmic and problem solving skills, especially in distributed or constrained compute environments. Preferred Skills: Experience with edge AI, federated learning, or offline inference systems. Understanding of AI governance and compliance frameworks relevant to public sector deployments. Experience integrating models into large scale distributed systems or microservice architectures. Excellent communication and technical documentation skills for collaboration across multi disciplinary teams. Strong understanding of GPU computing, CUDA, and performance profiling. We at webAI are committed to living out the core values we have put in place as the foundation on which we operate as a team. We seek individuals who exemplify the following: Truth - Emphasizing transparency and honesty in every interaction and decision. Ownership - Taking full responsibility for one’s actions and decisions, demonstrating commitment to the success of our clients. Tenacity - Persisting in the face of challenges and setbacks, continually striving for excellence and improvement. Humility - Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others. Benefits: Competitive salary Comprehensive health, dental, and vision benefits package 401(k) match (U.S.-based employees only) $200/month Health & Wellness stipend Continuing Education support $500/year Function Health subscription (U.S.-based employees only) Free parking for in-office employees Flexible Time Off (FTO) Parental leave for eligible employees Supplemental life insurance webAI is an Equal Opportunity Employer and does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, it is the policy of webAI to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works.
Machine Learning Engineer
Role Overview: As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine learning research into scalable, production-ready solutions. You will collaborate closely with cross-functional teams to identify opportunities where ML can drive product value, architect robust model-centric systems, and ensure their seamless integration into real-world applications. The role requires a strong balance between theoretical understanding and engineering execution, with a focus on building reliable, maintainable, and high-impact AI-driven features that align with Nace.AI ’s strategic objectives. Key Responsibilities: Design, build, and maintain end-to-end ML systems, including synthetic data pipelines, model training, debugging, and performance evaluation. Fine-tune large language models (LLMs) and implement meta-learning methods to enhance model generalization and efficiency. Improve existing Nace.AI models by incorporating advancements from recent ML research. Qualifications: Hands-on experience training and fine-tuning large language models (LLMs) and vision-language models (VLMs), including practical work with pre-training, instruction tuning, and alignment techniques (GRPO,RLHF/DPO/PPO). Hands-on Experience with Deep Learning Models, especially Transformers . Ability to translate cutting-edge research from papers into clean, production-ready code ( Paper to Code ). Proven experience scaling inference infrastructure for LLMs/VLMs, including expertise in model serving frameworks like vLLM , TGI. Proficient in Python with a strong track record of building substantial projects. Solid foundation in computer science fundamentals (data structures, algorithms, design patterns). BS degree in CS or related technical field. Solid Experience with ML frameworks and libraries (PyTorch, TensorFlow). Self-starter comfortable working in a fast-paced, dynamic environment. Preferred Qualifications: MS/PhD in CS or related technical field. Familiarity with data processing stacks such as Spark and Airflow. Experience with multi-node GPU training. Contributor to open-source ML projects. Deep knowledge in Linear Programming . Experience with advanced NLP and Multimodal post-training experience (e.g., model distillation, quantization, deployment optimization). Experienced in inference time optimization, deep understanding of LLM serving optimizations for LLMs/VLMs. Hands on experience with quantization techniques (AWQ, GPTQ, FP8/GGUF).
Sr. Computer Vision / Machine Learning Engineer
About the company Every physical good spends time in a warehouse, and every warehouse tracks their inventory. Today, nearly 100% of warehouses track their inventory manually using barcode scanners and climbing forklifts. We're Corvus Robotics . Our fully autonomous Corvus One ™ drones use computer vision & robotics to automatically track inventory, improving worker safety and increasing labor efficiency. We believe that data-driven, safe inventory management will optimize the global physical economy and improve economic prosperity for humanity. About the role We are hiring Computer Vision / Machine Learning Software Engineers to build compute-constrained models for deployed robots. You'll tackle diverse technical challenges, working with vast amounts of sensor data to increase environmental awareness and provide customers with deeper inventory insights. We value problem-solving, innovation, and continuous learning, and we encourage exploring new technologies to advance our machine learning capabilities. What you'll do Develop solutions for advanced computer vision tasks, including: Monocular and stereo depth estimation Learning-based structure-from-motion 3D occupancy networks Scene understanding Object detection Optimize performance, accuracy, and speed of compute-constrained models Collaborate across teams to deploy CV/ML models into production Improve ML pipelines and infrastructure for dataset management, training, and deployment Participate in R&D initiatives (20% of time) to experiment with state-of-the-art techniques Drive business value by leveraging your work across robotics, software, and deployment teams This role is in-person hybrid in Mountain View, CA. US work authorization is preferred but not required, can sponsor visas. Must have 5+ years of industry experience in Computer Vision / Machine Learning, Python/PyTorch Expertise in 2D and 3D computer vision techniques Proficiency with Linux, Git, AWS/GCP, and CI/CD workflows Experience in performance engineering for deep neural networks (both training and inference) Knowledge of model optimization techniques for embedded systems, including knowledge distillation, model quantization, and network pruning Adaptive and desire to assume responsibility in a fast-paced startup environment Nice to have Experience with C/C++ Familiarity with ROS (Robot Operating System) Knowledge of model deployment frameworks (e.g., TensorRT, RKNN, OpenVINO, ONNX, CoreML) Experience with TypeScript/JavaScript and Django Experience with data labelers, tooling, and data management
Senior/Principal Machine Learning Researcher - Bio...
About Cellular Intelligence Cellular Intelligence (CI) is an AI-native TechBio company building a universal foundation model of cell signaling to understand, predict, and ultimately control cellular behavior, transforming biology from trial-and-error into an engineering discipline. We take a full-stack approach, generating perturbation data at over 1,000x the efficiency of conventional methods, capturing millions of time-resolved treatment combinations per experiment, and training large-scale models that generalize across contexts to enable rational protocol design for regenerative medicine, context-specific drug effect prediction, and systematic disease modeling. Our founding team includes repeat AI entrepreneur Dr. Micha Breakstone ( Chorus.ai , acquired for $575M), the Head of the Fundamental AI Group at MIT, and four professors from Harvard Medical School and the University of Washington, including the Chair of Genetics at Harvard, collectively holding five National Academy memberships. Based in Boston, CI has raised over $60M to date from Khosla Ventures, CZI, SciFi VC, and AMD Ventures, and is rapidly scaling its world-class team. Cellular Intelligence's Core Values: We show up – fully accountable, all-in, doing whatever it takes We act with urgency – swift, decisive, proactive We support one another – collaborative, helpful, empathetic Location: Boston, MA (onsite, full-time) About the Role: As a Senior Machine Learning Researcher - Biological Foundational Models, you will play a key role in developing foundational models for single-cell RNA sequencing data. Working closely with other machine learning researchers and computational biologists, you’ll design cutting-edge AI solutions, contribute to pioneering research, and help build the core infrastructure driving Cellular Intelligence’s cell-replacement therapy platform. This role is ideal for a machine learning expert driven by scientific inquiry and eager to pioneer biological discoveries through foundational AI models. Responsibilities: Design, train, and optimize foundational models for single-cell RNA sequencing and other high-dimensional omics data Collaborate closely with computational biologists to ensure models produce biologically meaningful, interpretable outputs, engaging deeply with biological questions and research Design and implement novel deep learning architectures, including transformer-based models tailored to biological data Build scalable, distributed pipelines for training and inference across trillion-token biological datasets Provide mentorship and technical guidance to other machine learning researchers on the team Qualifications: 6+ years of experience in machine learning, transformer-based deep learning, and large-scale data analysis, preferably in biological applications Master’s degree or higher in Computer Science, Artificial Intelligence, Computational Biology, or a related field Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow Proven experience building and optimizing large-scale models, including transformer-based architectures Demonstrated track record of independent research and end-to-end model development, from prototyping to production Passion for AI-driven biology and its potential to transform healthcare Preferred Qualifications: Strongly preferred: PhD with a strong publication record in top machine learning and/or computational biology journals Strongly preferred: extensive background in biological research and biological modeling, ideally with single-cell RNA sequencing data Experience with reinforcement learning (e.g., RLHF, PPO), diffusion models, or multi-modal architectures applied to structured, high-dimensional data Experience in startups or fast-paced environments, with a self-directed, proactive work style Benefits: Take a technical leadership role with a mission-driven company with the potential to significantly impact the lives of millions Work alongside a talented and passionate team at the forefront of AI and cellular biology Contribute to the development of groundbreaking therapies that address significant unmet medical needs Enjoy a competitive salary and benefits package, including flexible work options Exceptional candidates who demonstrate outstanding capabilities and potential will be considered, even if they do not meet every qualification listed. Join us and help unlock the full potential of AI for the benefit of human health! Cellular Intelligence is committed to equal employment opportunity and non-discrimination for all employees and applicants regardless of race, color, ancestry, sex, sexual orientation, age, religion, national origin, ancestry, genetic information, disability, veteran status, marital status, citizenship status, or any characteristic protected under applicable law.
Machine Learning Engineer
About Finch We believe every American household deserves access to counsel in life’s biggest moments. At Finch, we’re building the infrastructure to make justice radically more accessible. Our modern approach to consumer law automates the admin work and puts clients first, starting with personal injury. In just over a year, we've grown 10x, raised a $20M Series A, and become the pre-litigation partner of choice for top personal injury firms across the country. We believe the best outcomes happen when expert operators and purpose-built AI work together – which is why we handle every step of pre-lit, from intake and claim opening to medical records, lien management, and demands, with humans leading every case. We’re backed by Sequoia, Redpoint, and the founders & CEOs of generational companies like DoorDash, Ironclad, and Digits. We’re rebuilding how the law serves everyday Americans from first principles, and we’re hiring exceptional operators to help us scale it nationwide. This Role Legal work is buried in unstructured documents, repetitive workflows, and data that no existing system handles well — and we're building the AI to fix it. As a Machine Learning Engineer at Finch, you'll own the full lifecycle of AI systems, from prototype to production, working on problems where a single breakthrough can meaningfully change how law firms operate. What You'll Do This is a hands-on applied AI role where you'll build and ship production systems — not just run experiments. The scope will grow as our product and team do. Build voice agents, browser agents, OCR pipelines, and LLM-powered workflows that work reliably in production. Design rigorous evaluation frameworks and feedback loops to systematically improve model accuracy and reliability. Own the full ML lifecycle — model selection, fine-tuning, prompt design, deployment, and monitoring. Collaborate directly with product, ops, and legal experts to make sure the AI is solving the right problems. Track emerging research and tools, and make deliberate calls about when to bring them into our stack. Who We Have in Mind This role is for engineers who care about outcomes over algorithms and are just as comfortable in production as they are in a notebook. Here's what that requires. Must-Haves 3+ years building and deploying production ML systems. Strong Python skills and experience working across the ML stack end-to-end. Hands-on experience with LLMs, prompt engineering, and evaluation design. A track record of shipping observable, maintainable AI systems — not just prototypes. Nice-to-Haves Experience with NLP, OCR, speech, or agent frameworks (LangChain, OpenAI APIs, etc.). Prior work at an early-stage startup where you helped define ML infrastructure from scratch. Familiarity with legal tech, document-heavy workflows, or regulated industries. This Role Might Not Be For You If You prefer research or experimentation over owning systems in production. You do your best work with a well-scoped problem and a stable, established ML platform. You're looking for a remote-first role — this one is 4 days/week in our NYC office. Compensation The expected package for this role includes base salary + commission + equity. Base salary = $180,000 - $280,000. Benefits 100% coverage for health, dental, and vision. 401(k) retirement plan. In-office snacks, drinks, and daily team lunches and dinners. Flexible PTO (we trust you to take the time you need). Interview Process Intro: An initial call to see if there's a mutual fit. Interview: An interview that typically involves a case assignment of sorts. Onsite: A visit to our NYC office to interview, meet the team, and have lunch. At Finch Legal, we believe in practicing what we advocate. As a company dedicated to upholding justice and protecting people in the workplace, we are equally committed to fostering a safe, inclusive, and equitable environment within our own walls. We welcome and support individuals from all backgrounds and lived experiences — regardless of race, ethnicity, gender identity, sexual orientation, religion, disability, or veteran status. We recognize that diversity strengthens our team, enriches our perspectives, and empowers us to better serve our clients and communities. At Finch Legal, inclusion isn’t just a value — it’s a practice.
Senior Machine Learning Engineer
Raspberry AI Raspberry AI is a leading provider of industry-defining AI design software for fashion brands and retailers. Our software empowers brands to rapidly understand consumer demand and create unique designs within minutes. Leveraging cutting-edge AI analytics and generative AI capabilities, we help fashion brands revolutionize their design and merchandising processes. We are a Series A startup, backed by top-tier venture capital firms such as Andreessen Horowitz, Khosla Ventures, MVP and Greycroft. About the Role This is a full-time remote role for a Senior Machine Learning Engineer at Raspberry AI. We are seeking a highly talented and motivated Machine Learning Engineer to join our growing ML team. In this role, you will focus on improving the quality and performance of our cutting-edge diffusion models, pushing the boundaries of generative AI in the fashion domain. Additional responsibilities may be assigned as business needs evolve. Responsibilities Conduct applied research and experimentation on state-of-the-art diffusion model architectures and training techniques. Implement and evaluate novel techniques for improving quality and controllability in generated designs. Analyze and interpret experimental results, draw meaningful conclusions, and communicate findings effectively. Collaborate closely with the team to translate prototypes into production-ready systems. Stay abreast of the latest advancements in diffusion models, deep learning, and generative AI research. Requirements Master's or Ph.D. in Computer Science, Machine Learning, or a related field. 3+ years of industry experience. Strong theoretical and practical understanding of deep learning, with a focus on generative models (e.g., GANs, VAEs, Diffusion Models). Hands-on experience with deep learning frameworks such as PyTorch. Experience with training and evaluating generative models on cloud GPU platforms (e.g., AWS, GCP, Azure). Proficiency in using and tuning multimodal LLMs, including experience with both API-based and open-source model implementations. Ability to effectively present complex technical information to both technical and non-technical audiences.
Senior · Staff · Principal Machine Learning Engine...
Senior / Staff / Principal Machine Learning Engineer Location: Onsite San Francisco (5 days onsite AND hybrid options) We have multiple startups interested in talent. Here is a generic summary. Instead of a perfect job description, we present talented individuals to companies and allow them to share how that talent fits in the organization. Key Responsibilities: Model Development: Designing and implementing ML algorithms and models, including deep learning models. Data Handling: Preprocessing, analyzing, and preparing large datasets for model training and evaluation. System Integration: Collaborating with software engineers to integrate ML models into production systems. Performance Optimization: Continuously improving and optimizing ML models for accuracy, efficiency, and scalability. Monitoring and Maintenance: Monitoring model performance in production, troubleshooting issues, and ensuring model reliability. Staying Updated: Keeping abreast of the latest advancements in ML, AI, and related technologies. Collaboration: Working with data scientists, software engineers, and other stakeholders to deliver effective ML solutions. Essential Skills: Programming Languages: Strong proficiency in Python, R, or other relevant languages. ML Frameworks: Experience with frameworks like TensorFlow, PyTorch, or scikit-learn. Data Science Fundamentals: Solid understanding of statistical analysis, data modeling, and machine learning algorithms. Problem-Solving: Excellent analytical and problem-solving skills to address complex challenges. Communication: Effective communication skills to convey technical information to both technical and non-technical audiences. Collaboration: Ability to work effectively in a team environment. Education and Experience: A bachelor's or master's degree in computer science, engineering, mathematics, statistics, or a related field is typically required. Several years of experience in machine learning, data science, or software development is often preferred. Compensation: Market range and can include equity – details can be provided after the specific client is determined.
Machine Learning Engineer, Senior
Location: Onsite — Austin, TX Employment Type: Direct Hire, Full‑Time Job Title: Senior ML Engineer Company Overview 9 Mothers Defense develops AI-enabled systems to counter unmanned aerial threats. Our first product, EDDA, is an autonomous counter-sUAS point-defense platform designed to detect, track, and neutralize Group 1 drone threats. The company is headquartered in Austin, Texas. Position Summary 9 Mothers is seeking a Machine Learning Engineer to design, train, and maintain the models that power our counter-sUAS perception stack. The Machine Learning Engineer is responsible for model research, dataset engineering, and the training infrastructure that supports detection, classification, and discrimination of aerial targets. This is an individual contributor position. We don’t use RAG, LLMs, or pre-built cloud APIs. Our stack requires building from the ground up to solve high-stakes problems under strict Size, Weight, and Power (SWaP) constraints. You should be capable of building models from scratch and have a fundamental understanding of the problem space. Essential Duties Design, train, and iterate on machine learning models for detection, classification, and tracking of aerial targets. Own the dataset pipeline end-to-end, including data collection, labeling, curation, augmentation, synthetic data generation, and closed-loop retraining based on field performance. Build and maintain training infrastructure, including experiment tracking, compute orchestration, and evaluation harnesses. Define metrics and evaluation methodologies that correlate to real-world operational performance. Support deployment of trained models into the production perception stack, and address discrepancies between training and deployed performance. Analyze field data to identify and address model failure modes. Requirements Demonstrated experience shipping machine learning systems into production under real-world operational requirements. Fluency in PyTorch or JAX, including full training loop development beyond fine-tuning off-the-shelf models. Strong software engineering skills beyond model development, including ownership of training infrastructure. Proficiency in Python; working knowledge of C++ or Rust at the training-to-deployment boundary. U.S. citizenship and ability to pass a background check. Preferred Qualifications Experience with detection or tracking of small, fast, or adversarially perturbed targets. Synthetic data generation and sim-to-real methodologies. Experience training models for edge deployment, including quantization-aware training and knowledge distillation. Prior experience in defense or other safety-critical machine learning applications. Active security clearance, or eligibility to obtain one. Passion for building robots or engineering projects as a hobby Benefits Meaningful Early Equity: You aren't just an employee; you are a foundational owner. Your contributions directly drive the value of your stake in the company. Direct Roadmap Influence: Forget the bureaucracy of big defense. You will have a seat at the table, directly shaping our product and technology trajectory from day one. Mission-Critical Work: We don't build for "what if." We build systems the Department of War actively needs to counter immediate, real-world threats. The Builder's Playground: Work in a brand-new lab fully optimized for rapid prototyping, equipped with NVIDIA Jetsons, high-end scopes, and 3-D printers. 100% Employer-Paid Premiums: We cover 100% of your medical, dental, and vision insurance premiums and cover 50% of healthcare premiums for your dependents. Unlimited PTO: We value results, not clock-watching. Take the time you need to stay sharp and recharge. Zero Red Tape: You report to the founders. You have the autonomy to make technical decisions that would take months of committee approval at a larger firm. Austin-Based Culture: Join an onsite team in Austin, TX, where we prioritize high-bandwidth collaboration and rapid field-testing. Relocation Assistance: We want the best talent in the room. If you aren't in Austin yet, we’ll help you get here, to make your transition to the Silicon Hills seamless. About the Interview 1. Application screen phone call (30 min) 2. Live coding test with the Director of Engineering (45 min via MS Teams) 3. Paid take-home consulting agreement ($500, ~4-8 hours) 6. Onsite interview in Austin 7. Offer Equal Employment Opportunity 9 Mothers is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status. Export Control This position may require access to information or technology subject to U.S. export control laws and regulations, including the International Traffic in Arms Regulations (ITAR). U.S. citizenship is required.
Machine Learning Scientist
About Suno We're building the world's first creative entertainment platform, where the entire world can feel the joy and fulfilment of making music. Music is for everyone: Our users include everyone from grandmothers creating songs for their loved ones, to Grammy winners using Suno Studio, our power tool, to make the most popular hits in the world. Building the future of entertainment requires ambition. The pace is fast, the problems are hard, and the work demands ownership and intensity. For the right people, it’s incredibly rewarding: a chance to shape a new medium, work with a small team that cares deeply about quality, make music, drink too much coffee, and build something that millions of people use to express themselves in ways that were never before possible. Suno is the fastest growing consumer entertainment company and the leader in AI music. We are backed by leading investors including Bond Capital, Menlo Ventures, Lightspeed Venture Partners, IVP, Forerunner, Union Square Ventures, Alkeon, Quiet, Matrix Partners, Schroders Capital and, NVentures (venture arm of NVIDIA). About the Role We’re looking for early members of our research team. You’ll work closely with the founding team and have ownership of a wide variety of technical decisions on how we build and deploy our state of the art ML models trained with an H100/scientist ratio of >100x. Check out our Suno version of the job here: https://suno.com/s/YaZAme1qvoqFJOi1 What You’ll Need 5+ years experience training state of the art models with distributed pytorch Intimate familiarity of the entire stack of data engineering, designing, training and evaluating machine learning models Track record showing independent ownership of entire research projects from start to finish Extensive experience training large generative models from scratch (LLMs or diffusion models) A love of music (listening, exploring, making) is a huge plus Bachelor's degree or equivalent required. Additional Notes: Applicants must be eligible to work in the US. Compensation The annual salary range for this role is $160K – $280K + target equity + benefits (including medical, dental, vision, and 401k) Perks & Benefits for Full-Time Employees Company Equity Package 401(k) with 3% Employer Match & Roth 401(k) Medical, Dental, & Vision Insurance (PPO w/ HSA & FSA options) 11 Paid Holidays + Unlimited PTO & Sick Time 16 Weeks of Paid Parental Leave Creative Education Stipend Generous Commuter Allowance In-Office Lunch (5 days per week) Suno is proud to be an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the Massachusetts Fair Chance in Employment Act, NYC Fair Chance Act, LA City Fair Chance Ordinance, and San Francisco Fair Chance Ordinance.
Lead Machine Learning Engineer
At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses. The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity. Who We Are We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully. This role develops and scales large-scale machine learning training systems for multimodal robotics data, enabling the creation of high-performance autonomy models. By optimizing distributed training pipelines, neural network architectures, and data processing workflows, the position improves training efficiency, accelerates model iteration, and maximizes GPU utilization. The role collaborates closely with ML researchers and infrastructure teams, influencing the design, deployment, and performance of end-to-end autonomy models and the large-scale data pipelines that support them. Responsibilities Design and maintain training systems that can process and learn from petabyte-scale multimodal datasets (e.g., video and point cloud data). This includes ensuring data is efficiently loaded, distributed, and processed across large GPU clusters. Identify and resolve bottlenecks in the training pipeline, including data loading, preprocessing, model computation, and inter-node communication, to maximize GPU utilization and reduce training time. Work with the ML team to develop and refine neural network architectures suitable for autonomy tasks, particularly those handling high-dimensional and sequential sensor data. Create and adjust loss functions and training strategies that help the model learn effectively from complex multimodal inputs and improve autonomy performance. Configure, monitor, and maintain large-scale distributed training jobs across multiple machines and GPUs, ensuring stability, fault tolerance, and efficient resource usage. Implement scalable systems to preprocess, transform, and augment large robotics datasets so that they are suitable for model training. Work closely with ML scientists and other engineers to integrate new models, experiments, and training approaches into the production training pipeline. Analyze training metrics, model outputs, and experiment logs to assess model performance and guide improvements in architecture, data usage, or training strategies. Develop tools and workflows that allow teams to run experiments, track results, and iterate quickly on new model ideas or training approaches. Qualifications Master’s or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a closely related technical discipline. Minimum of 5 years of professional experience developing, training, and deploying machine learning models in production environments. Hands-on experience training machine learning models across multiple GPUs or compute nodes, including familiarity with distributed training frameworks and large dataset handling. Strong programming skills in Python for implementing machine learning models, data pipelines, and training workflows. Solid knowledge of core concepts such as neural networks, optimization algorithms, loss functions, model evaluation, and training methodologies. What Makes You Stand out Experience identifying and resolving training bottlenecks related to compute utilization, memory usage, and data throughput in machine learning systems. Experience training machine learning models on robotics or autonomous driving datasets involving multimodal sensor inputs such as camera video, LiDAR point clouds, radar, or telemetry data. Experience developing models that combine multiple data modalities (e.g., images, point clouds, and structured sensor data) into a unified learning system. Peer-reviewed publications or significant research contributions in machine learning, robotics, or related areas. * Please note: The listed base salary range applies to candidates based in the US. Compensation may vary depending on location, experience, and role alignment. We are open to qualified candidates working remotely in Canada Canada - ALL: $177k - $215k CAD
Research Intern (PhD), Machine Learning
Output has built a biological reasoning model that understands biology at the scale and complexity life actually operates. Our model independently learned the principles of molecular interactions, opening up drug treatments that were previously impossible. We're already generating therapies that traditional approaches cannot reach. The hardest problems in both AI and biology are being solved here, and there is room for you to own one. Output is currently in stealth, operated by a team of repeat founders and biotech veterans with multiple exits in AI x Bio, and backed by top-tier VCs including Y Combinator. Our internships offer flexible commitment, with a minimum of 20 hours per week, ranging 12 to 24 weeks. We have various start dates available to accommodate your academic schedule. There may be opportunities for full-time employment upon successful completion of your PhD. The Role You will own a research project that directly advances Output's research and its path to new therapies. This is not a side project: your work will contribute to the same models and methods the full-time team builds on. We will select a project together based on your research interests and our priorities, with a path to publishing your work at top-tier venues and the opportunity to continue with additional projects throughout the year. About You You are currently pursuing a PhD in machine learning, computer science, computational biology, physics, mathematics, or a related field You have a strong research track record, demonstrated by publications or submitted work at venues such as NeurIPS, ICML, ICLR, or relevant computational biology conferences You have hands-on experience designing and running ML experiments, including training models and analyzing results You are proficient in Python and PyTorch, and comfortable working with large-scale datasets and GPU infrastructure You can work independently on a research problem: scoping an approach, running experiments, interpreting results, and communicating findings clearly Bonus Points You have experience applying machine learning to biological, chemical, or molecular data You have a background in computational biology, biophysics, chemistry, or a related natural science You have experience with generative models, representation learning, or self-supervised learning You have contributed to open-source machine learning or computational biology projects Our Values ❤️ Heart: We foster a culture of ownership. We are assembling a team of individuals who are passionate and take pride in their contributions. 🏆 Excellence: We have an unwavering commitment to excellence and continuously challenge ourselves to reach the highest standards. 🚀 Practicality: We value practicality and results-oriented thinking. We are committed to making a tangible impact on the lives of patients and the broader community. 📣 Honesty: We place a high value on honesty and directness. We firmly believe in addressing issues as they arise, in an open and transparent manner. 🎮 Fun: We believe that life is too short to not have fun. Our goal is to create a workplace that is fun, engaging, rewarding and fulfilling. What We Offer We encourage new and different ideas, creativity and contrarian thinking Healthy feedback focused environment to help you strive - leadership will have high expectations, regularly share constructive feedback, support you and help you grow, and welcome receiving feedback and ideas from you You own your day-to-day management. What we care about is that we all hit our milestones Competitive salary and equity in a growing, well-funded startup Excellent medical, dental, and vision coverage
Senior Machine Learning Specialist
You will be responsible for developing and implementing machine learning solutions that power our AI-native platform. This role requires expertise in custom model development, transformer architectures, and production deployment of ML systems. Responsibilities Design and develop custom machine learning models for specific business use cases. Implement and optimize transformer architectures for natural language processing tasks. Develop and maintain ML pipelines for data preprocessing, model training, and inference. Work with large language models and implement fine-tuning strategies. Implement retrieval-augmented generation (RAG) systems and optimize their performance. Collaborate with engineering teams to deploy ML models in production environments. Monitor and maintain model performance in production, implementing retraining strategies as needed. Conduct research on emerging ML techniques and evaluate their applicability to our platform. Mentor junior ML engineers and contribute to best practices within the team. Required Skills and Qualifications Master's degree or PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience. 5+ years of experience in machine learning development and deployment. Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow, or similar). Deep understanding of transformer architectures and their applications in NLP. Experience with large language models and fine-tuning techniques. Proficiency in implementing and optimizing RAG systems. Experience with ML model deployment and production monitoring. Strong understanding of data preprocessing, feature engineering, and model evaluation techniques. Experience with cloud platforms and ML infrastructure (AWS SageMaker, Google Vertex AI, or similar). Excellent problem-solving skills and attention to detail. Strong communication and interpersonal skills. Preferred Qualifications Experience with MLOps and ML pipeline orchestration tools. Knowledge of distributed training and model optimization techniques. Experience with vector databases and similarity search algorithms. Familiarity with reinforcement learning and multi-agent systems.
Machine Learning - Infrastructure
Our mission is general causal intelligence, AI that is capable of (1) predicting the future and (2) identifying the optimal actions to change that future. To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because domains governed by physics have inherent cause and effect relationships, unlike visual or textual data. Weather is the ideal training ground for an LPM. It is the most well-observed physical system, offering rapid, objective ground truth feedback from sensory observations and data at a scale that dwarfs what is used to train today’s LLMs. Causal Labs is a team of researchers and engineers from self-driving, drug discovery, and robotics - including Google DeepMind, Cruise, Waymo, Insitro, and Nabla Bio - who believe general causal intelligence will be the most important technical breakthrough for civilization. We look for infrastructure engineers who are excited to tackle unsolved problems. Our training and inference challenges demand deep expertise in setting up distributed training clusters and optimizing performance for large models. If you have experience building large-scale ML infrastructure in related fields such as language and vision models, robotics, biology -- join us on this mission. Responsibilities Design, deploy, and maintain large distributed ML training and inference clusters Develop efficient, scalable end-to-end pipelines to manage petabyte-scale datasets and model training throughout the entire ML lifecycle Research and test various training approaches including parallelization techniques and numerical precision trade-offs across different model scales Analyze, profile and debug low-level GPU operations to optimize performance Stay up-to-date on research to bring new ideas to work What we’re looking for We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains. Strong grasp of state-of-the-art techniques for optimizing training and inference workloads Demonstrated proficiency with distributed training frameworks (e.g. FSDP, DeepSpeed) to train large foundation models Knowledge of cloud platforms (GCP, AWS, or Azure) and their ML/AI service offerings Familiarity with containerization and orchestration frameworks (e.g., Kubernetes, Docker) Background working on distributed task management systems and scalable model serving & deployment architectures Understanding of monitoring, logging, observability, and version control best practices for ML systems You don’t have to meet every single requirement above.
Senior Machine Learning Engineer
Orchard Robotics is a Series A startup backed by top VCs like Quiet Capital, Shine Capital, and General Catalyst. We're securing America’s food supply by building the AI farmer that automates our nation’s farms. We've raised over $25M in pursuit of our mission to help farmers farm more profitably and sustainably than ever before. What We Do: We start by collecting the most valuable data for farmers, telling them everything about what is growing on their millions of trees, across thousands of acres of farmland. We do this using advanced camera systems we build, that take pictures of every one of the billions of fruit in a farm. This data lives in our cloud data platform, FruitScope, that we've developed from the ground up to help farmers manage their crops with precision. Farmers across the nation use our industry-leading software to look at their data, make critical decisions, and command farming operations on a daily basis. Our technology is used today across some of the largest farms in the nation. The Role: In order to analyze billions of fruit on farms all year long, our advanced, tractor-mounted camera systems have to know a.) precisely where they are, and b.) everything about the fruit they are seeing. We are looking for a Senior Machine Learning Engineer to build creative, practical, and robust solutions to ML/CV software and infrastructure problems, relating to training edge ML models on massive amounts of real-world farm image data collected by our camera systems. About the role: As an early engineer, you'll receive generous equity compensation Full-time role at our San Francisco, CA office Flexible working hours Comprehensive Health, Vision, and Dental coverage, and we cover 100% of the premium We move fast, and sometimes this means staying late or working weekends Our team is close-knit & highly driven, you’ll work directly with our CEO and entire team We’re deeply motivated by the impact we’re making – every line of code written or new system built means less food that goes to waste, and more people who are fed. What you’ll do: Build and maintain scalable ETL pipelines for processing large, diverse image datasets collected from our tractor-mounted camera systems in farms. Stay up-to-date with current literature in computer vision models and architectures, and apply relevant advancements to our systems. Develop, deploy, and monitor infrastructure for model training, evaluation, and inference, both in the cloud and on edge devices. Design and implement intelligent active sampling infrastructure to optimize data collection and improve model performance. Collaborate with a multidisciplinary team to integrate ML solutions into production robotics systems. Work closely with agronomists and farmers to understand crop biology and translate domain knowledge into actionable ML features. Be a generalist, supporting different parts of our software stack as needed. What makes you a good fit: 5+ years of experience building production-grade data pipelines and ML infrastructure. Proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch). Strong experience with data engineering tools (e.g., Pandas, SQL, Apache Airflow, Spark). Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Experience working with massive amounts of real-world training data. Familiarity with MLops software and data engineering to ensure consistent deployment of ML models. Ability to work independently, learn quickly, and operate in a dynamic environment Enthusiasm for taking on multiple roles and responsibilities as our company grows. Bonus Points: Experience deploying & optimizing ML models to run fast on embedded compute like NVIDIA Jetson Experience prototyping, evaluating, or deploying new ML/CV models on the edge. If you're looking to help make a positive impact in the world by building the future of farming, come join us!
Machine Learning Engineer
Orchard Robotics is a Series A startup backed by top VCs like Quiet Capital, Shine Capital, and General Catalyst. We're securing America’s food supply by building the AI farmer that automates our nation’s farms. We've raised over $25M in pursuit of our mission to help farmers farm more profitably and sustainably than ever before. What We Do: We start by building AI-powered camera systems that collect the most valuable data for farmers, telling them everything about what is growing on their millions of trees, vines, and plants, across thousands of acres of farmland. Our state-of-the-art AI analyzes every one of the billions of fruit across a farm. We provide accurate yield estimates, fruit counts, size projections, disease detection, inventories, bloom maps, and more! All this data lives in our cloud platform, FruitScope OS, that we've developed from the ground up to enable farmers to manage their crop with precision. Today, our technology is trusted by some of the largest farms in the nation. We are growing fast, and have the industry-leading product. Farmers use our software every day to make critical decisions and run more efficient, profitable operations. The Role: In order to analyze billions of fruit on farms all year long, our advanced, tractor-mounted camera systems have to know a.) precisely where they are, and b.) everything about the fruit they are seeing. We are looking for a Machine Learning Engineer to build creative, practical, and robust solutions to ML/CV software and infrastructure problems , relating to training edge ML models on massive amounts of real-world farm image data collected by our camera systems. About the role: Full-time, in-person role at our San Francisco or Seattle office. As an early engineer, you'll receive generous equity compensation Comprehensive Health, Vision, and Dental coverage, and we cover 100% of the premium We move fast, and sometimes this means staying late or working weekends Our team is close-knit & highly driven, you’ll work directly with our CEO and entire team We’re deeply motivated by the impact we’re making – every line of code written or new system built means less food that goes to waste, and more people who are fed. What you’ll do: Build and maintain scalable ETL pipelines for processing large, diverse image datasets collected from our tractor-mounted camera systems in farms. Develop and deploy infrastructure for model training, evaluation, and inference, both in the cloud and on edge devices. Design and implement intelligent active sampling infrastructure to optimize data collection and improve model performance. Stay up-to-date with current literature in computer vision models and architectures, and apply relevant advancements to our systems. Collaborate with a multidisciplinary team to integrate ML solutions into production robotics systems. Work closely with agronomists and farmers to understand crop biology and translate domain knowledge into actionable ML features. Be a generalist, supporting different parts of our software stack as needed. What makes you a good fit: 2+ years of real-world, industry experience building production-grade data pipelines and ML infrastructure. Proficiency in Python and experience with ML frameworks (e.g., PyTorch). Strong experience with data engineering tools (e.g., Pandas, SQL, MLFlow, WandB). Familiarity with cloud platforms (AWS, GCP) and containerization (Docker, Kubernetes). Experience working with massive amounts of real-world training data. Familiarity with MLops software and data engineering to ensure consistent deployment of ML models. Ability to work independently, learn quickly, and operate in a dynamic environment Enthusiasm for taking on multiple roles and responsibilities as our company grows. Bonus Points: Experience deploying & optimizing ML models to run fast on embedded compute like NVIDIA Jetson Experience prototyping, evaluating, or deploying new ML/CV models on the edge. If you're looking to help make a positive impact in the world by building the future of farming, come join us!
Machine Learning Engineer (Foundation Models & Per...
Join the Sleep Fitness Movement At Eight Sleep, we’re on a mission to fuel human potential through optimal sleep. As the world’s first sleep fitness company, we’re redefining what it means to be well-rested and building the most advanced hardware, software, and AI technology to make it possible. Our products power peak mental, physical, and emotional performance by transforming every night of sleep into a personalized, data-driven recovery experience. We are trusted by high performers, professional athletes, and health-conscious consumers in over 30 countries worldwide. Recognized as one of Fast Company's Most Innovative Companies in 2019, 2022, and 2023, and twice named to TIME's “Best Inventions of the Year.” We operate like a high-performance team: fast, focused, and motivated by impact. We don’t just ship; we iterate, refine, and obsess over the details that help our members sleep better and wake up stronger. Every role at Eight Sleep is a chance to create cutting-edge technology, collaborate with world-class talent, and help shape a future where sleep isn’t passive - it’s a powerful tool for living better. If you’re tired of the ordinary and driven to build at the edge of what’s possible, this is your moment. Join us and lead the movement that’s transforming how the world sleeps and what we’re all capable of when we wake up. High Standards. No Apologies We operate with intensity because our mission demands it. At Eight Sleep, we bring the same mindset as the world’s top performers: focused, relentless, and always pushing to be in the top 1% of our craft. Think Kobe Bryant’s mamba mentality, applied to bold ideas, next-gen tech, and flawless execution. This isn’t a 9-to-5. We’re a team that puts in the extra effort, not because it’s required, but because we care about the impact of our work. We’re here to build fast, push limits, and deliver without compromise. If you thrive under pressure and want to do the most meaningful work of your career, you’ll feel right at home. If you’re looking for something easier – this isn’t it. The role We’re looking for a Machine Learning Engineer to build and ship consumer-facing AI systems that power personalization, coaching, and next-generation “sleep intelligence.” You’ll work across data, modeling, product, and engineering to translate research into reliable, measurable improvements for members. This role is ideal for someone who loves end-to-end ownership: from problem framing → prototyping → offline evaluation → online experimentation → production deployment → iteration. How you’ll contribute Build and deploy ML models that improve sleep experiences through personalization, prediction, and behavior understanding (e.g., readiness forecasting, event detection, individualized recommendations). Apply and adapt foundation-model capabilities to real product workflows (LLM + tools/RAG, multimodal modeling, policy learning), including MCP-style integrations where helpful. Develop user behavior models that connect longitudinal signals (sleep, environment, routines) to actionable interventions - grounded in robust experimentation and measurement. Design evaluation strategies (offline metrics, slice-based analysis, calibration, reliability, fairness) and partner with Product to run high-quality online experiments. Productionize models: scalable training/inference pipelines, model monitoring, drift detection, alerting, and continuous improvement loops. Collaborate with cross-functional partners (Product, Mobile, Backend, Clinical) to scope requirements and ship high-impact features. What you need to succeed Minimum Qualifications 2+ years building ML systems in production, ideally for consumer-facing products. Strong ML fundamentals across supervised learning, sequence/time-series modeling, and modern deep learning. Hands-on experience with large-scale model training and evaluation (PyTorch/TensorFlow/JAX), and strong Python engineering practices. Experience with personalization systems (ranking/recommendations, segmentation, lifecycle modeling, propensity/behavior modeling, causal/experiment-aware thinking). Fluency with data tooling (SQL, distributed compute such as Spark/Ray, and cloud storage/compute). Strong product sense: you can translate ambiguous goals into measurable outcomes and iterate quickly with stakeholders. Bonus Points Experience applying LLMs/foundation models to product features (tool use, retrieval, structured outputs, guardrails, evals). Experience with multimodal data (sensor signals + context) and/or health/biometrics data. Experience with privacy-preserving approaches (on-device/federated learning, differential privacy, data minimization). Experience designing experimentation frameworks or causal inference approaches for personalization. Why join Eight Sleep? Innovation in a culture of excellence Join us in a workplace where innovation isn’t just encouraged - it’s a standard. Our flagship product, the Pod, is a testament to our culture of excellence, beloved by hundreds of thousands of customers worldwide. At Eight Sleep, you will be part of a team that continuously pushes the boundaries of technology in sleep fitness. Immediate responsibility and accelerated career growth From your first day, you’ll take on substantial responsibilities that have a direct impact on our core business and product success. We are a small team that empowers you to own your projects and see the tangible effects of your efforts, enhancing both your professional growth and our company’s trajectory. Your path will be challenging but rewarding, perfect for those who thrive in fast-paced environments aiming for high standards. Collaboration with exceptional talent Work alongside other bright minds like you: at Eight Sleep exceptional intelligence and a passion for breakthroughs are the norms. Our team members are not only experts in their fields but also avid innovators who thrive in our dynamic, fast-paced environment. Equitable compensation and continuous equity investment We extend equity participation to every full-time team member, recognizing and rewarding your direct contributions to our success. This includes periodic equity refreshments based on performance, ensuring that as Eight Sleep grows and succeeds, so do you – perfectly aligning your achievements with the broader triumphs of the company. Pay grows rapidly as you accumulate experience with Eight Sleep and translate it into concrete impact. Your own Pod - and other great benefits Every Eight Sleep employee receives the very product that defines our mission: a Pod of their own. If you join us you’ll get your own Pod, along with other benefits. Learn more at eightsleep.com/careers At Eight Sleep we continually celebrate the diverse community different individuals cultivate. As an equal opportunity employer, we stay true to our values by ensuring everyone feels they can flourish and grow. We are committed to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Machine Learning Engineer - Fraud Risk
About the Company Rain makes the next generation of payments possible across the globe. We’re a lean and mighty team of passionate builders and veteran founders. Our infrastructure makes stablecoins usable in the real-world by powering card transactions, cross-border payments, B2B purchases, remittances, and more. We partner with fintechs, neobanks, and institutions to help them launch solutions that are global, inclusive, and efficient. You will have the opportunity to deliver massive impact at a hypergrowth company that is funded by some of the top investors in fintech, crypto, and SaaS, including Sapphire Ventures, Norwest, Galaxy Ventures, Lightspeed, Khosla, and several more. If you’re curious, bold, and excited to help shape a borderless financial future, we’d love to talk. Our Ethos We believe in an open and flat structure. You will be able to grow into the role that most aligns with your goals. Our team members at all levels have the freedom to explore ideas and impact the roadmap and vision of our company. About the Team The fraud risk management team at Rain creates sophisticated, scalable risk mitigation solutions to protect our customers and deliver a low-friction experience. We achieve this by maintaining transaction and lifecycle event monitoring, building alerts to speed fraud detection and response, and creating risk rules and strategies powered by ML models. We are a pillar of the business, supporting new products and ensuring their success. Rain’s next-generation payment technology introduces new fraud vectors that require holistic, end-to-end thinking, strong data fundamentals, and fraud management savvy to combat. What you’ll do Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis Develop and maintain end-to-end ML pipelines: data ingestion, feature engineering, model training, deployment, and continuous monitoring Design and implement low-latency, real-time decision systems partnering with fraud risk data scientists, integrating with transaction or behavioral data streams Own ML infrastructure, including model versioning, automated retraining, and safe deployment strategies (e.g., shadow, rollback) Build robust monitoring and alerting for model performance, latency, data quality, and drift Lead experimentation on model explainability, drift detection, and adversarial robustness for fraud prevention use cases Develop tooling and processes to improve the effectiveness and speed of the ML development lifecycle Partner with platform teams to meet strict SLAs for availability, latency, and accuracy Collaborate closely with talented engineers, data scientist and compliance teams across Rain Work in a fast-paced environment on a rapidly growing product suite Solve complex problems at the intersection of ML systems, data, and reliability What we're looking for 5+ years of experience building ML systems in production; at least 2+ in fraud, risk, or anomaly detection domains A degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field Proven track record designing and maintaining ML models at scale Advanced proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn) Strong understanding of supervised/unsupervised learning, anomaly detection, and statistical modeling Ability to work autonomously, manage ambiguity, and collaborate closely with data scientists to translate analytical models into robust fraud prevention systems Experience developing, validating, and productionalizing predictive real-time and offline fraud detection models using supervised and unsupervised ML techniques Experience collaborating with cross-functional teams to prioritize, scope, and deploy MLI solutions at scale Nice to have, but not mandatory Domain expertise in banking, payments, or transaction monitoring Experience with graph-based or network-level fraud detection techniques A graduate degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field Experience fine-tuning or adapting generative AI / large language models for pattern generation or synthetic data augmentation (in partnership with data science) Knowledge of model governance, bias mitigation, and regulatory compliance in fraud contexts Things that enable a fulfilling, healthy, and happy experience at Rain: Unlimited time off 🌴 Unlimited vacation can be daunting, so we require Rainmakers to take at least 10 days off. Flexible working ☕ We support a flexible workplace. If you feel comfortable at home, please work from home. If you’d like to work with others in an office, feel free to come in. We want everyone to be able to work in the environment in which they are their most confident and productive selves. New Rainmakers will receive a stipend to create a comfortable home environment. Easy to access benefits 🧠For US Rainmakers, we offer comprehensive health, dental, and vision plans for you and your dependents, as well as a 100% company subsidized life insurance plan. Retirement goals 💡Plan for the future with confidence. We offer a 401(k) with a 4% company match. Equity plan 📦 We offer every Rainmaker an equity option plan so we can all benefit from our success. Rain Cards 🌧️ We want Rainmakers to be knowledgeable about our core products and services. To support this mission, we issue a card for our team to use for testing. Health and Wellness 📚 High performance begins from within. Rainmakers are welcome to use their card for eligible health and wellness spending like gym memberships/fitness classes, massages, acupuncture - whatever recharges you! Team summits ✨ Summits play an important role at Rain! Time spent together helps us get to know each other, strengthen our relationships, and build a common destiny. Expect team and company off-sites both domestically and internationally.
Machine Learning Engineer, Model Evaluations (Spee...
About Plaud Inc. Plaud is building the world's most trusted AI work companion for professionals to elevate productivity and performance through note-taking solutions, loved by over 1,500,000 users worldwide since 2023. With a mission to amplify human intelligence, Plaud is building the next-generation intelligence infrastructure and interfaces to capture, extract, and utilize what you say, hear, see, and think. Plaud Inc. is a Delaware-incorporated, San Francisco-based company pushing the boundary of human–AI intelligence through a hardware–software combination. With SOC 2, HIPAA, GDPR, ISO27001, ISO27701, and EN18031 compliance, Plaud is committed to the highest standards of data security and privacy protection. To learn more about Plaud, please visit https://www.Plaud.ai and follow along on Instagram , X , Facebook , LinkedIn , and YouTube Why You Should Join Us Plaud is building the next generation intelligence infrastructure and interfaces to capture, extract, and utilize intelligence from what people say, hear, see, and think. Plaud is a bootstrapped, skyrocketing, profitable company with a $250M revenue run rate achieved in just three years. Define the next-gen paradigm for human-AI interaction. Gain exposure to cutting-edge AI for Pro tools and play a direct role in our global expansion. Work with passionate teammates who value innovation, collaboration, and customer success. Grow your career in a culture that champions continuous learning and fast career development. Market-competitive compensation, global exposure, and a vibrant, creativity-fueled work atmosphere. You may be a good fit if you: Have a passion for turning ambiguous, subjective concepts like a voice's naturalness, expressiveness, or conversational cadence into clear, defensible, and automated metrics that researchers and leadership can rely on. Possess strong software engineering skills (especially in Python) and have experience building reliable distributed systems, data pipelines, or evaluation harnesses that can run at scale against live model checkpoints. Can deeply partner with ML researchers to define exactly what "good" looks like for a Speech LLM, translating capabilities (like ASR robustness in noisy environments or TTS emotional steerability) into measurable benchmarks. Are comfortable building and owning dashboards that track model health during training, improving signal-to-noise ratios, reducing evaluation latency, and making performance regressions impossible to miss. Rapidly debug anomalous mid-training results to determine if a drop in performance stems from the model architecture, corrupted data, or infrastructure. Communicate complex statistical results and model behaviors clearly to both technical and non-technical stakeholders. Strong candidates may also have experience with: Speech Metrics: Deep familiarity with both traditional (WER, CER, PESQ, etc) and modern audio evaluation frameworks (automated MOS scoring). LLM-as-a-Judge: Using frontier models or finetune multi-modal LLMs to evaluate the conversational logic, transcription accuracy, audio quality, and reasoning of audio models. Human Evaluation: Managing large-scale crowdsourcing operations or preference data collection to support RLHF/DPO efforts. Observability: A strong background in statistics and experimental design, paired with experience building trusted tracking dashboards (e.g., Weights & Biases, MLflow). Adversarial Datasets: Curating complex datasets to test edge cases, such as heavy accents, overlapping speech, or highly noisy acoustic environments. What We Offer Founding Team Initiative: Opportunity to be an early, foundational member of our core SpeechLLM lab, with meaningful ownership and impact on a fast-growing startup. Competitive Compensation: $200K - $365K base salary + performance bonus + Equity. Comprehensive Benefits: Top-tier healthcare for employees and dependents, including dental and vision, and a generous employer subsidy. Retirement Planning: 401(k) plan for full-time employees with company matching. Paid Time Off: Unlimited PTO, plus 13 paid holidays. New Parent Leave: 12 weeks of paid time off to spend time with your new family, regardless of gender. Hybrid Office: Minimum of 3x in-office per week to foster highly collaborative, fast-paced research. Gear & Perks: Choice of top-of-the-line laptops/workstations, annual offsites, and a fully stocked office. Plaud is and will continue to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristics.
Sr. Engineer, Machine Learning
About Poshmark Poshmark is the leading fashion marketplace where style comes alive through discovery, self-expression, and human connection. Powered by a vibrant community of 165 million members, Poshmark brings real people and taste to shopping through a social experience shaped by shared discovery. Buying and selling fashion feels simple, joyful, and personal, while every item tells its own story. Poshmark empowers sellers to grow meaningful businesses, keeps fashion in circulation longer, and gives shoppers access to unique and trusted finds, from everyday pieces to one-of-a-kind vintage and luxury. The Machine Learning team is a central player in the Poshmark organization. Our mission is to build a world-class machine learning platform to bring value out of data for us and for our customers. Our goal is to democratize data science and machine learning, support exploding business, and use machine learning to drive value across the chain (Search, personalization, fraud detection, catalog digitization to name a few). Responsibilities: Manage the entire ML lifecycle from data collection to deployment and monitoring Collaborate across teams such as DS, QA, Infra and other engineering teams to productionize ML models Write and optimize code for production environments, ensuring the robustness and reliability of ML services at scale Manage and support current solutions while evolving them to incorporate newer technologies Strong written, verbal, and presentation skills, with the ability to convey complex concepts in a clear and simple manner Stay updated with the latest developments in data science and machine learning Desired Skills & Experience: 4+ years of experience applying Machine Learning to concrete problems at large scale Bachelors or Masters in Computer Science, Statistics, or related field Strong CS fundamentals. Should be able to write algorithms with ease. Solid understanding of Data Science and ML fundamentals – Regression, Classification, Tree-based approach, Neural network, and sequence-based models. Working experience with at least one ML model: LLMs, GNN, Deep Learning, Logistic Regression, Gradient Boosting trees, etc. Should have excellent understanding of ML lifecycle Good understanding of system architecture. Have knowledge of big data technologies – streaming architecture, data pipelines, etc. Experience with Python, SQL, Java or Scala Good to Have: Big data systems – Spark, EMR, S3, AirFlow Production experience with LLMs and prompt engineering Experience with Flask, FastAPI, RabbitMQ, Embeddings and Vector DBs Our Techstack: Pytorch, Tensorflow, Sklearn Flask, Fastapi, Gunicorn, Quarkus MLFlow, Sagemaker, Kibana, Airflow, Databricks, Grafana, Datadog Docker, Kubernetes, Jenkins Redis, Redshift, MongoDB, Spark, Kafka, RabbitMQ, Milvus
Machine Learning Engineer
Compare.com is a start-up company that has changed the way consumers shop for car insurance in the United States. We are Great Places to Work Certified, are expanding our product breadth and are looking to add the best talents to our team. Are you up for the challenge? If so, keep reading. Who will love this job: This position is a great opportunity for an entrepreneurially - minded person who enjoys making a difference in a start-up environment. We are looking for an experienced Machine Learning Engineer to complement the team to help create and extend existing models. Compare.com views Machine Learning (ML) as an integral component of our strategy, and we want you to help us build ML as a competency for Compare.com from the ground up. The ideal candidate will have strong analytical curiosity and a passion for applying advanced modelling techniques in problem solving. He or she must be self-motivated, dependable, and determined team player and be able to communicate complex concepts in simple terms and can tie analytic results to business outcomes. What you'll be responsible for: Building extending, deploying, maintaining, and troubleshooting ML solutions to help drive business metrics. Developing and initiating projects from start to finish. Collaborating with developers, product owners and other team members to rapidly build, test and deploy robust algorithms and data analytic solutions. Collaborating with Analytics and Data teams to optimize ML model performance and data pipelines. Ensuring our relevancy in the field of ML by staying up to date on the latest ML techniques and become the subject matter expert within the company. Being an advocate for data science, working with senior managers throughout the business, supporting helping them understand where and when it can add value to their processes. Developing solutions to complex problems without considerable technical direction. Creation of meaningful datasets for modelling and evaluation from a vast quantity of data in multiple formats. Communicating results to key decision-makers Other duties as assigned - we're a start-up! You are an ideal candidate for this role if you: Technical: Degree in quantitative discipline (Statistics, Math, Data Science, Computer Science, Engineering) 2+ years of experience as a machine learning engineer or data scientist Experience in the end-end deployment of Machine Learning Models Proven and demonstrated experience with data preparation, building, scaling, and optimizing machine learning solutions 2+ years of experience with developing software in object-oriented programming languages, including Python, Scala, Java, or C++ Experience with machine learning tools, including TensorFlow, Pytorch, or Scikit Learn Experience with DB structured query language (e.g. SQL) High degree of comfort and experience operating within an Agile environment (e.g., Kanban, Scrum) Experience utilizing or building CI/CD pipelines Experience utilizing and implementing components in cloud-based infrastructure (e.g., Azure, Amazon Web Services) Proficiency writing code and understanding object-oriented paradigms Non-Technical: Are self-driven, organized, dependable and a determined team player Have a positive outlook and desire to drive and instill data quality throughout the organization Think analytically and solve complex problems within a team structure Are flexible and able to adapt to new processes and requirements in a dynamic environment Have strong, clear, and concise written and verbal communication skills Can effectively communicates technical information to non-technical people as and when required Are an advocate for innovation and drive change through persuasion and consensus Can translate issues/problems into solutions with tangible changes that improve the team or the organization Are flexible and able to adapt to new processes and requirements in a dynamic environment Have a positive outlook and desire to drive and instill Technical Excellence within the IT dept. Have strong, clear, and concise written and verbal communication skills Can communicate technical information to non-technical people as and when required Can plan and manage your own workload within multiple projects, delivering results within agreed deadlines Can proactively demonstrate new ideas and innovation At this time, Compare.com will not sponsor a new applicant for employment authorization for this position. Compare.com is an equal opportunity employer committed to diversity in the workplace and promotes a drug-free environment. While we are open to remote work, we will not be able to support employees based out of CA, CO, MA, NY or NJ. Our Business Compare.com is a website which is reinventing how consumers shop for services in the US. Our aim is to bring new transparency to the auto insurance market and to help people save money. We intend to be the same for other online comparison shopping, branching out to other related insurance and unrelated financial services. Our People & Culture So, what's it like to be an employee of Compare.com? At Compare.com, every day is different, and we are different, maybe even a bit counterculture. Flexibility is key, as we plan for long-term but adjust continuously. We firmly and wholeheartedly believe that people who like what they do, do it better and we go out of our way to ensure that coming to work is enjoyable. Doing the job and doing it well is the key no matter who you are. At Compare.com we are a team, we work as a team and if we fall, we fall as a team and lean on each other to pick us up. Effective interaction with the team is required, as is challenging each other's ideas to come to the best possible solution. We are looking for someone who comes with strong problem-solving skills and a wealth of good ideas on their own, but also recognizes the inherent creativity in all of us and accepts willingly that the next great idea may not be their own genesis. Compare.com offers an honest and open culture and every member of our staff is treated as an equal. Our benefits and 401k package is highly competitive, as is our work/life balance, flexible work schedules, and generous PTO structure. Achievement is recognized and rewarded, but most of all coming to work is fun. We work hard to produce high quality work, but we like to play as well. These days that involves more Slack trivia, Drawasaurus and Zoom cooking classes together than it does ping pong and happy hours. Benefits & Perks Competitive Compensation, Bonus Program, & Stock Scheme Health, Dental, Vision, and Disability coverages HSA + generous employer contribution annually 401k + company match Generous PTO + Flexible Hours Paid Parental Leave program Wellness Fund Training & Development Stipend
Machine Learning & Big Data Researcher
Machine Learning & Big Data Researcher – Network and Security Group Open Positions : multiple Overview We are looking for a highly motivated researcher to partner with our network and security group to conduct research and development in the areas of machine learning & big data. Job Duties · Conducting research and development · Design and implement machine learning, deep learning and big data analytics for different types of data. Examples of data types include text, network, security, time series, social media, and scientific data. · Design, implement and test software and hardware prototypes · Summarize research results, write research reports and proposals Required skills & abilities · Previous experience in one or multiple of the related areas, including but not limited to: distributed computing, big data, internet of things, cloud computing, machine learning, text analytics, networking, embedded systems, mobile devices, and cyber security · Proficient programming skills using Python, Java, or C/C++ · Good writing and communication skills Education requirements · A degree (BS, MS or PhD) and/or a research background in the areas of Computer Science, Computer Engineering and/or Electronic Engineering ABOUT IAI Intelligent Automation, Inc. (IAI) is a technology innovation company headquartered in Rockville, MD. For over 30 years, we have specialized in providing advanced technology solutions and R&D services to federal agencies and corporations throughout the United States and internationally. Leveraging agile R&D processes, a multi-disciplinary collaborative environment, and its substantial intellectual property portfolio, IAI excels in developing concepts into market-focused products and customer-driven solutions. IAI’s core R&D areas include: Air Traffic Management, Big Data and Social Media Analytics, Control and Signal Processing, Cyber Security, Education and Training Technologies, Health Technologies, Modeling and Simulation, Networks and Communications, Robotics, and Sensor Systems. US Citizenship or Permanent Residency required All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
Summer Intern - Big Data & Machine Learning
Summer Intern - Big Data & Machine Learning
Machine Learning (ML) Engineer
About Velocity-X: Velocity-X, a VelocityBlack company, constructs and deploys data management and analytics solutions for the defense and intelligence communities. We're proud to boast a world-class engineering team that thrives on rolling up their sleeves to solve your mission's biggest challenges. Velocity-X is seeking a highly motivated and self-directed professional to fill the role of Machine Learning (ML) Engineer to support our team in Northern Virginia. Responsibilities: Design, develop, and implement machine learning models and algorithms to solve specific business problems. Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. Transform machine learning models into deployable APIs and integrate them with existing applications and infrastructure. Collaborate closely with data scientists, software engineers, and product managers to understand requirements and translate them into practical ML solutions. Experiment with different machine learning techniques and algorithms to identify the most effective approaches for given problems. Evaluate model performance using appropriate metrics and iterate on models to improve accuracy, efficiency, and scalability. Monitor and maintain deployed models, ensuring their reliability and performance in production environments. Troubleshoot and resolve issues related to machine learning models and pipelines. Stay up-to-date with the latest advancements in machine learning, deep learning, and related fields. Contribute to the development of best practices and standards for machine learning development and deployment within the team. Document machine learning models, experiments, and deployment processes. Potentially work with large datasets and big data technologies. Optimize machine learning models for performance and efficiency. Qualifications: Master's in computer science, Machine Learning, or higher level degree is preferred with of 3+ years of related industry experience in Machine Learning, Computer Science, Data Science or related fields. Demonstrated hands-on experience in developing and deploying machine learning models in a production environment. Strong programming skills in Python and experience with relevant machine learning libraries and frameworks such as TensorFlow, Keras, PyTorch, scikit-learn, etc. Solid understanding of machine learning algorithms (e.g., regression, classification, clusting, dimensionality reduction, deep learning architectures). Experience with data preprocessing, feature engineering, and data visualization techniques. Familiarity with data storage and processing technologies (e.g., SQL, NoSQL databases, Spark, Hadoop). Experience with cloud platforms (e.g., AWS, Azure, GCP) and their machine learning services. Understanding of software development principles, version control (e.g., Git), and CI/CD pipelines. Strong analytical and problem-solving skills with the ability to interpret data and draw meaningful conclusions. Excellent communication and collaboration skills to effectively communicate technical concepts to both technical and non-technical audiences. Preferred Skills: Experience with specific areas of machine learning such as Natural Language Processing (NLP), Computer Vision, or Recommender Systems. Experience with MLOps practices and tools for automating and monitoring machine learning workflows. Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes. Experience with building and deploying RESTful APIs. Familiarity with big data technologies and distributed computing. Experience with statistical modeling and inference. Position Clearance Requirement: TS/SCI with Full-Scope Polygraph Equal Opportunity Employer: Velocity-X is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law.
Machine Learning Engineer
nou Systems, Inc. (nSI) is a 100% ESOP company solving challenging defense problems in an enjoyable, stimulating environment. We support mission areas including missile defense, battle management, cybersecurity, test range modernization, biotechnology, and space control. We are pleased to announce an opening for a Machine Learning Engineer who will join our team in Colorado Springs, Colorado. You will help design, develop, and deploy machine learning capabilities across research, prototype, and operationally relevant defense applications. We are looking for a mid-level engineer with strong fundamentals in machine learning, backend software development, and data science. The ideal candidate can collaborate with teammates to move ML capabilities from concept toward usable systems. Responsibilities Develop, train, evaluate, and analyze machine learning models using Python and PyTorch, writing modular, maintainable, and testable code. Build backend software components that support ML workflows, data processing, model evaluation, and system integration. Help identify, prepare, and validate training and evaluation datasets. Implement and adapt ML methods from open literature, including supervised learning and deep learning approaches. Work in Linux-based, containerized development environments using VS Code Dev Containers, Remote-SSH, Docker/Podman, and contribute to continuous improvement of development processes. Use GitLab-based workflows for source control, issue tracking, merge requests, code review, and collaboration. Communicate technical results clearly through written documentation, presentations, and team discussions. Support multiple project teams and help translate ML concepts into practical engineering solutions. Basic Qualifications Bachelor's degree in computer science, mathematics, software engineering, data science, or a closely related technical field. 3+ years of professional experience in machine learning, data science, or backend software development. Hands-on experience developing, training, or evaluating deep learning models using PyTorch. Professional experience building backend software, data pipelines, APIs, or system integration components. U.S. citizenship and the ability to obtain a Secret security clearance. Preferred Qualifications Strong communication skills, intellectual curiosity, and comfort working across ML, software, and mission-domain teams. Our work often requires creative, multidisciplinary approaches. Understanding of core ML and statistical concepts (bias-variance tradeoff, data mismatch, sample sufficiency) Comfort with self-direction. You'll work with our top technical talent, but we're looking for evidence you can diagnose a problem and approach it strategically. Experience with Docker, Podman, or similar containerization tools, including editing Dockerfiles or container configuration. Experience with ML tools (MLflow, Optuna, or PyTorch Lightning) Experience with RL development using Gymnasium and Ray RLlib. Experience with LLMs, generative AI tools, vector databases, or frameworks such as LangChain or LlamaIndex. Familiarity with CI/CD pipelines, Kubernetes, DevSecOps practices, secure artifact repositories, or deployment in restricted DoD environments. Position Details This is an in-office role requiring presence at least 4 days per week. Annual Salary Range: $100,000 - $160,000 nou (pronounced 'new') Systems: Since its founding in 2012, nou Systems has built a reputation for excellence in innovative engineering, prototype development, and technical and professional services in multiple markets, including missile defense, cybersecurity, test range modernization, and space control. Our diverse and highly skilled team delivers state-of-the-art products from concepts and prototypes to fully developed and integrated solutions. We offer significant advancement and personal career development opportunities within our dynamic high-tech company. Our culture is firmly established in treating our employees like family. Benefits of working at nSI nou Systems, Inc. offers a comprehensive, total rewards package that includes competitive compensation and diverse benefits that reflect our company culture of service, excellence, and a supportive work environment. Benefits may vary based on status but the majority of our positions include the following: Competitive Wages* Medical, Rx, Dental & Vision Insurance Medical plan with Health Savings Account eligibility Generous company-funded Basic Life Insurance Company-funded Short-Term & Long-Term Disability Optional Accident and Critical Illness Insurance Personal Time Off, Annual Leave, and Paid Federal Holidays 401(k) Retirement Plan Employee Stock Ownership Plan (ESOP) Tuition Reimbursement for ongoing training, continuing education, or advanced degree programs Personal Development, Learning Opportunities, & Lunch-n-Learns Opportunities for Advancement Skills Development & Certifications Employee Referral Bonus Program Corporate Sponsored Events & Community Outreach *Final compensation for this position is determined by a variety of factors, such as a candidate's relevant work experience, skills, certifications, and geographic location. nSI is an Equal Opportunity Employer Employment opportunities at nSI are based upon a candidate's qualifications and capabilities to perform the essential functions of a particular job and are free from discrimination based on race, color, religion, national origin, sex, sexual orientation, gender identity, age, disability, protected veteran status, genetic information, or any other characteristic protected by law. For our complete EEO/AA and Pay Transparency statement, please visit www.nou-systems.com/workingatnou . U.S. citizenship is required for most positions.
Software Engineer-Data Engineering, Machine Learni...
Position Summary: The IT Division is responsible for the development and operations of information systems for the State and Federal agencies doing business related to or using information from the administration of motor vehicles and driver licenses. The Machine Learning (ML) Data Engineer position has core responsibilities for the design, development, deployment, and operational support of machine learning solutions on cloud infrastructure. This includes the full model lifecycle - from data acquisition and dataset preparation through feature engineering, experimentation, model training, validation, production deployment, and ongoing monitoring. Current applications include anomaly detection across high-volume messaging networks, but the scope encompasses any ML capability that strengthens system reliability, operational intelligence, and data-driven decision-making across AAMVA systems. Essential Duties and Responsibilities: We are seeking a talented Data Engineer with machine learning experience to join our team. You will design, build, and operationalize ML solutions running on cloud infrastructure (Azure or AWS). You will work across the full model lifecycle: preparing datasets, engineering features, running experiments, deploying models to production, and operating them on cloud infrastructure. As a detail-oriented professional, you have a strong track record of independently managing projects and driving them to successful completion. Your statistical foundation and engineering discipline enable you to move from exploratory analysis through to production-grade, monitored solutions. You communicate clearly with both technical and non-technical stakeholders - translating model behavior, data constraints, and engineering trade-offs into terms that drive decisions. You operate effectively across the broader IT organization, with sufficient general IT fluency to understand how ML systems interact with infrastructure, security, operations, and business workflows, and you proactively build those connections rather than working in a data silo. Key responsibilities include: Designing and building dataset preparation pipelines - acquiring, cleaning, transforming, and versioning data for ML training and evaluation Engineering features that extract meaningful signals from structured and semi-structured data sources (time-series patterns, statistical profiles, categorical encodings) Running structured experimentation - testing multiple algorithms against defined scenarios, measuring performance, and documenting findings Training, evaluating, and tuning ML models including regression, classification, clustering, anomaly detection, and ensemble methods Deploying models to production on cloud infrastructure and building the pipelines that keep them running (retraining, scoring, threshold management) Monitoring model performance in production - tracking drift, false positive rates, and detection efficacy over time Building and maintaining batch and streaming data pipelines using Synapse, Fabric, Spark, and Event Hubs that feed ML systems Writing and optimizing analytical queries (SQL, KQL, PySpark) for data exploration, statistical profiling, and real-time analysis Creating validation frameworks - synthetic test data generation, backtesting against historical logs, and shadow-mode evaluation Building dashboards and visualizations that communicate model outputs to technical and non-technical stakeholders Collaborating with cross-functional teams to identify ML opportunities and translate operational problems into data solutions; communicating findings, trade-offs, and model behavior clearly to technical and non-technical audiences across IT, operations, and leadership Direct Reports: None QUALIFICATIONS Formal Education: Bachelor's degree in computer science, data science, statistics, mathematics, or related quantitative field. Equivalent work experience may be substituted Knowledge, Skills, and Abilities: Basic Qualifications 3-5 years of hands-on experience in data engineering, ML engineering, or applied analytics Hands-on cloud platform experience (Azure or AWS) building and deploying data or ML solutions on managed cloud services; specific platform less important than depth of experience Working knowledge of statistical foundations: distributions, variance, standard deviation, trend vs. seasonality, hypothesis testing, and how to apply them to real operational data Experience with the ML experiment-to-production cycle: dataset preparation, feature engineering, model training, evaluation, and deployment Proficiency in Python for data processing, statistical analysis, and ML model development Strong SQL skills with understanding of relational database fundamentals: data modeling, query optimization, indexing strategies, and how SQL Server infrastructure supports production workloads (T-SQL, stored procedures, Availability Groups) Experience building data pipelines that handle batch and streaming workloads Experience with version control systems (Git) and CI/CD practices Strong problem-solving skills, attention to detail, and ability to work independently on ambiguous problems Strong written and verbal communication skills - able to explain technical findings to non-technical stakeholders and engage productively across IT, operations, and leadership; comfort operating outside the ML silo and contributing to broader technology discussions Preferred Qualifications Experience with time-series analysis, anomaly detection, or statistical process control on operational data Familiarity with unsupervised and semi-supervised techniques (isolation forest, clustering, ensemble methods) Experience building and managing ML model lifecycle on Azure (MLflow, Fabric ML, Azure ML) or AWS (SageMaker, Glue, Step Functions) Familiarity with KQL (Kusto Query Language) for time-series decomposition, log analytics, or real-time data exploration Knowledge of data modeling and dimensional modeling concepts Experience with synthetic test data generation and model validation frameworks Familiarity with operations and monitoring of mission-critical data platforms Technical Stack Core Technologies: Microsoft Fabric, Azure Synapse Analytics, Apache Spark, Delta Lake, Azure Event Hubs ML & Analytics: scikit-learn, PySpark ML, statistical modeling, time-series analysis, feature engineering, model validation Languages: Python, SQL, PySpark, KQL, C# Data Infrastructure: T-SQL, Stored Procedures, SQL Server Availability Groups Azure Services: Azure Functions, Azure Data Factory, Azure Key Vault Optional: Databricks, Snowflake, Lakehouse Architecture, Azure OpenAI; AWS candidates: equivalent services (SageMaker, Glue, Kinesis, Redshift) are acceptable in place of Azure-specific stack items Visualization: Power BI Development: Azure DevOps, CI/CD Disclaimer Statement: The preceding job description has been written to reflect management's assignment of essential functions. It does not prescribe or restrict the tasks that may be assigned. AAMVA is an Equal Opportunity Employer/Veterans/Disabled
AI & Machine Learning Transportation Analyst
POSITION OVERVIEW Intelligent Automation, Inc. (IAI) is seeking team members who are outstanding candidates with a background in both AI /machine learning and traffic engineering at all levels (Entry, Mid, and Senior). Candidates will work in a highly agile and dynamic R&D environment that focuses on applications of AI/machine learning to transportation research, traffic management, and operations. The candidates should support (and/or lead) the research, development, and integration of Machine and Deep Learning in the areas of traffic data, video, time series signal analysis. JOB DUTIES • Depending on the experience and interest, job duties will include: • Design and implement AI and deep learning algorithms for traffic detector/video data, time series data, social media, network, and text analytics • Design and develop the architecture and framework for a cloud-based analytics-as-a-service platform to handle high volume traffic data • Design, develop and integrate web services to support existing software applications • Design, enhance, and maintain existing software and perform upgrades to existing live production software applications • Support monitoring of deployed services and applications DESIRED SKILLS • Prior experience with one or more of the following technologies is a plus, but not required. • Eager and motivated to perform research, learn and develop new techniques. • Machine/Deep Learning Libraries: TensorFlow/Keras/PyTorch, OpenCV, Matlab • Hands on experience with Deep Convolutional Neural Network, Deep Reinforcement Learning • Programming languages/platforms: Python, Java/JDK, HTML5, JavaScript, C/C++ • Database experience: MongoDB, MySQL/SQL, InfluxDB, Prometheus, Accumulo • Traffic Simulation Software Tools, such as VISSIM • Operating Environment/Services: Linux, Windows, Android/iOS EDUCATION A bachelor's degree (or higher) in Computer Science, Engineering, Civil or related field is required. ABOUT IAI Intelligent Automation, Inc. (IAI) is a technology innovation company headquartered in Rockville, MD, conducts state-of-the-art research and product development in the areas of Robotics, Signal Processing and Control, Sensor Technology, Distributed Intelligent Systems and other areas (visit www.i-a-i.com). We specialize in providing advanced technology solutions and R&D services to federal agencies, and corporations throughout the United States and internationally. IAI's core R&D areas include Air Traffic Management, Big Data, and Social Media Analytics, Control and Signal Processing, Transportation Research/Development and Services, Cyber Security, Education, and Training Technologies, Health Technologies, Modeling and Simulation, Networks and Communications, Robotics and Sensor Systems. For more information on IAI, please visit www.i-a-i.com. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status. IAI is an EEO/AA employer.
Summer Intern - Big Data & Machine Learning
Summer Intern - Big Data & Machine Learning
Engineering Manager, AI Game Engine
ABOUT US Weekend https://www.weekend.com/ (formerly Volley https://www.volleygames.com/) is the leading developer of voice AI games for smart TVs. Our games attract millions of users every month, with family favorites like Jeopardy!, Song Quiz, CoComelon: Sing and Play with JJ, and Wheel of Fortune. We believe voice interfaces will become the main way people access entertainment in their living rooms, kitchens, bedrooms, and cars. We’re building a bundled subscription product similar to Netflix or Spotify for the emerging category of “AI-powered games.” Weekend was founded by Max Child and James Wilsterman in 2024. The founders went through Y Combinator in 2018 and landed a spot on the 2022 YC Top Companies List. We’re a quickly-growing team headquartered in Union Square, San Francisco. ROLE SUMMARY We’re looking for an experienced Engineering Manager to lead our AI Game Engine team. You will lead a high performing team of software and ML engineers building the systems and services needed to deliver best-in-class voice-first AI-native gaming experiences to our players on connected TVs and other gaming surfaces. This is a new team responsible for building and evolving our core voice and model integrations along with other services needed to deliver our game development and runtime platform. This includes speech-to-text and text-to-speech pipelines, LLM game integration, model evaluation and fine tuning, image generation pipelines, and content/prompt management. You will work closely with our Game and Platform teams as well as cross functional partners in Product and Data to drive our AI and game engine strategy. You will identify impactful opportunities, rapidly ship new features, and your products to support current and future players. We’re actively expanding our game titles and platforms and this is an opportunity to play a pivotal role in creating innovative, fun applications in voice-activated gaming across connected devices. This job opportunity offers a hybrid work arrangement, and we are specifically seeking candidates who are comfortable with on-site attendance in our Bay Area office 3 times per week and are either currently residing in or near the Bay Area or willing to relocate for on-site work. WHAT YOU'LL DO AT WEEKEND - Lead and grow a high performing team of engineers, ensuring we build the right things at the right time to support our evolving gaming ecosystem. - We are an AI-first development shop - evangelize and leverage AI productivity and development tools within your team and help drive adoption across the company. - Partner closely with Product and Game teams to define the roadmap, set priorities, deliver delightful and impactful features. - Build and scale systems and services to enable fun, engaging and unique player experiences leveraging STT/TTS, LLMs and other core technologies. - Collaborate with platform and infrastructure teams to identify upstream and downstream dependencies, align delivery schedules, and streamline release processes. - Foster a culture of ownership, continuous learning, and product thinking within the team. - Ensure the technical excellence of the team by providing feedback, promoting best practices, and helping remove roadblocks. WE'RE EXCITED ABOUT YOU BECAUSE YOU HAVE - 2+ years managing engineering teams, with a proven record of cultivating productivity and team satisfaction. - Demonstrated ability to build and grow high performing engineering teams - 5+ years of hands-on software engineering experience, ideally in fast-paced consumer product environments. - Experience successfully building and evolving AI integrations leveraging STT/TTS/LLM and similar models. - Demonstrated cross-functional collaboration, with strong communication skills and a bias toward action. - Experience building and running scalable services and/or software platforms including monitoring, alerting and incident response. BONUS POINTS - Experience and personal interest with gaming, interactive media, a
Senior Data Scientist, Game Engine Simulation
Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. WHY DATA SCIENCE & ANALYTICS? The Data Science & Analytics organization's mission is to increase our speed, frequency and acumen of making decisions at scale by instilling a data-influenced approach to building products. We cover a wide area of the data spectrum including analytical data engineering, product analytics, experimentation, causal inference, statistical modeling and machine learning. Aligned and partnering with product verticals, we use this extensive tool belt to discover new opportunities and unmet use cases, influence and shape the product roadmap and prioritization, build data products and measure impact on our community of players and developers. WHY GAME ENGINE AND DIGITAL MATTER The Digital Matter team, within the Engine organization, powers Roblox’s Game Engine Simulation capabilities. Digital Matter sits at the core of how Roblox worlds are built, experienced, and brought to life. Spanning Physics, Geometry, Rendering, and Interactivity, this team defines the fundamental systems that govern how worlds behave, look, and respond – making balls bounce, light scatter, meshes stream, avatars move, and experiences feel immersive across every device. As the Senior Data Scientist on this team, you will partner with engineers and product leaders to shape the technical foundation of the platform. Your work will directly influence how we simulate reality and empower creators to bend it – balancing physical realism with performance, scalability, and artistic control. You will drive data-informed decisions across core engine systems, defining and evolving top-line metrics for performance, stability, visual quality, scalability, and user experience. Your impact will span the entire stack – from physics simulation and mesh streaming (SLIM), to rendering pipelines and input systems – ensuring that engine innovations translate into measurable improvements in creator capability and player experience. Collaborating closely with engineering, product, and leadership, you will evaluate the impact of foundational engine changes, uncover performance bottlenecks, guide prioritization through rigorous experimentation, and surface new opportunities through dashboards, deep analysis, and strategic experimentation. In a team that builds the substrate of the platform, your work will help determine how the next generation of Roblox experiences looks, feels, and performs. You Will: - Define Success Metrics for Digital Matter – Establish key performance indicators for Physics, Geometry, Rendering, and Interactivity, such as performance, latency, stability, me
Senior AI Designer
WHAT MAKES US EPIC? At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating. Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development. DESIGN What We Do Put your design skills to work at a cutting-edge company that challenges the norm. From character and level design to competitive gameplay and system design, help shape the future of interactive entertainment at Epic. What You'll Do Epic Games is looking for an experienced Senior AI Designer to join our cross-disciplinary development team, where you will own the vision and create innovative features. In this role, you will - Own the design vision for AI-focused features. - Craft high-quality, player-facing AI behaviors and abilities using behavior trees and visual scripting. - Create quick prototypes to try out and demonstrate new ideas in the game - Collaborate with team members from various disciplines to deliver high-quality experiences to our players - Cooperate with other designers from the AI team to define and promote a cohesive vision for AI in your project - Use playtest feedback to iterate and improve AI features - Create and maintain clear and concise design documentation What we're looking for - Experience shipping multiple AAA titles as a Designer - Experience developing AI for a highly systemic environment - Experience creating behavior trees and using visual scripting - A creative approach that can surprise and amaze players with new ideas and experiences - Talent for empathizing with players and a deep understanding of player motivations. - Team player with good communication skills (verbal and written) EPIC JOB + EPIC BENEFITS = EPIC LIFE Our intent is to cover all things that are medically necessary and improve the quality of life. We pay 100% of the premiums for both you and your dependents. Our coverage includes Medical, Dental, a Vision HRA, Long Term Disability, Life Insurance & a 401k with competitive match. We also offer a robust mental well-being program through Modern Health, which provides free therapy and coaching for employees & dependents. Throughout the year we celebrate our employees with events and company-wide paid breaks. We offer unlimited PTO and sick time and recognize individuals for 7 years of employment with a paid sabbatical. ABOUT US Epic Games is a leading interactive entertainment company. For over 30 years we've been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fid
Senior Data Scientist II
Alma is on a mission to simplify access to high-quality, affordable mental health care. We do this by making it easy and financially rewarding for therapists to accept insurance and offer in-network care. When a provider joins Alma, they gain access to a suite of tools that not only help them better run their business, but also grow it sustainably and develop as a provider. Alma is available in all 50 states, with over 20,000 therapists in our growing network. Anyone looking for a therapist can browse Alma’s free directory. Alma has raised $220.5M in funding from Insight Partners, Optum Ventures, Tusk Venture Partners, Primary Venture Partners, First Round Capital, Sound Ventures, BoxGroup, Cigna Ventures, and Rainfall Ventures. Alma was also named one of Inc’s Best Workplaces in 2022 and 2023. Website Job Board Values Candidate Interview Guide --- Senior Data Scientist II The Data Science team is integral to Alma's mission, utilizing data to inform business strategy and drive product innovations. We are seeking a Senior Data Scientist II in the Client Experience domain, focusing on helping clients connect with the right providers. You will leverage your expertise in analytics, experimentation, and AI/ML to address complex challenges including conversion funnel optimization, search ranking, and client-provider matching. As the lead data scientist in this domain, you will own the execution of key strategic initiatives and mentor other data scientists on the team. This is a highly cross-functional position, requiring close collaboration with Product, Engineering, and Marketing stakeholders to define the strategy and roadmap for the Client Experience domain. What you’ll do: - Define Data Science strategy and priorities in the Client Experience domain <li style="font-size: 12pt; font-family: helvetica, aria
Staff Machine Learning Research Scientist, LLM Eva...
As the leading data and evaluation partner for frontier AI companies, Scale is dedicated to advancing the evaluation and benchmarking of large language models (LLMs). We are building industry-leading LLM evals, setting new standards for model performance assessment. Our mission is to develop rigorous, scalable, and fair evaluation methodologies to drive the next generation of AI capabilities. Our Research teams work with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. As a Staff Machine Learning Research Scientist on the LLM Evals team, you will lead the development of novel evaluation methodologies, metrics, and benchmarks to measure the capabilities and limitations of frontier LLMs. You will help define what "good" looks like in generative AI, driving research that informs both our internal roadmap and the broader research community. This role is critical for designing and executing a roadmap that defines best practices in data driven AI development and will accelerate the next generation of generative AI models in partnership with top foundational model labs. You will: - Drive research on the effectiveness and limitations of existing LLM evaluation techniques. - Design and develop novel evaluation benchmarks for large language models, covering areas such as instruction following, factuality, robustness, and fairness. - Communicate, collaborate, and build relationships with clients and peer teams to facilitate cross-functional projects. - Collaborate with internal teams and external partners to refine metrics and create standardized evaluation protocols. - Implement scalable and reproducible evaluation pipelines using modern ML frameworks. - Publish research findings in top-tier AI conferences and contribute to open-source benchmarking initiatives. - Mentor and guide research scientists and engineers, providing technical leadership across cross-functional projects. - Stay deeply engaged with the ML research community, tracking emerging work and contributing to the advancement of LLM evaluation science. - Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results. Ideally you'd have: - 5+ years of hands-on experience in large language model, NLP, and Transformer modeling, in the setting of both research and engineering development - Experience and track of recording in landing major research impacts in a fast-paced environment - Experience tech leading a team of research scientists and research engineers - Excellent written and verbal communication skills - Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals - Previous experience in a customer facing role. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific sala
Tech Lead/Manager, Machine Learning Research Scien...
As the leading data and evaluation partner for frontier AI companies, Scale is dedicated to advancing the evaluation and benchmarking of large language models (LLMs). We are building industry-leading LLM evals, setting new standards for model performance assessment. Our mission is to develop rigorous, scalable, and fair evaluation methodologies to drive the next generation of AI capabilities. Our Research teams work with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. As the Tech Lead Manager of the LLM Evals Research team, you will lead a talented team of research scientists and research engineers focused on developing and implementing novel evaluation methodologies, metrics, and benchmarks to assess the capabilities and limitations of our cutting-edge LLMs. This role is critical for designing and executing a roadmap that defines best practices in data driven AI development and will accelerate the next generation of generative AI models in partnership with top foundational model labs. You will: - Lead a team of highly effective research scientists and research engineers on LLM evals. - Conduct research on the effectiveness and limitations of existing LLM evaluation techniques. - Design and develop novel evaluation benchmarks for large language models, covering areas such as instruction following, factuality, robustness, and fairness. - Communicate, collaborate, and build relationships with clients and peer teams to facilitate cross-functional projects. - Collaborate with internal teams and external partners to refine metrics and create standardized evaluation protocols. - Implement scalable and reproducible evaluation pipelines using modern ML frameworks. - Publish research findings in top-tier AI conferences and contribute to open-source benchmarking initiatives. - Remain up-to-date on ongoing research in the team, help work through technical challenges, and be involved in design decisions - Remain deeply involved in the research community, both understanding trends, and setting them - Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results. Ideally you'd have: - 5+ years of hands-on experience in large language model, NLP, and Transformer modeling, in the setting of both research and engineering development - Experience and track of recording in landing major research impacts in a fast-paced environment - Experience supporting and leading a team of research scientists and research engineers - Excellent written and verbal communication skills - Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals - Previous experience in a customer facing role. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific
Data Scientist, Product
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As part of our growing Data Science and Analytics team, you will play an instrumental role in our company’s mission of building safe and beneficial artificial intelligence by driving data-informed decision making across our organization. You’ve worked in cultures of excellence in the past, and are eager to apply that experience to help shape the cultural norms and best practices of a growing data science team as Anthropic continues to scale. In this unique company, technology, and moment in history, your work will be critical to informing our strategy as we deploy safe, frontier AI at scale to the world. Responsibilities: - Define core metrics, build measurement frameworks, and maintain core reporting to evaluate success - Deep dive into product and user data to derive actionable insights and size opportunities to improve products, strategy and operations, influencing roadmaps through your insights and recommendations - Develop hypotheses, apply rigorous causal inference methods – controlled experiments, synthetic controls – and analyze the results in order make actionable recommendations - Investigate anomalies, conduct root cause analyses, and provide data-driven insights to guide priorities and inform decisions - Build statistical models, optimization frameworks, and simulations to automate decision-making and operational processes - Present complex technical analyses and recommendations to both technical and non-technical stakeholders - Establish foundational data practices and help scale our analytics infrastructure to support rapid iteration and decision-making as our products grow You may be a good fit if you have: - 7+ years of experience in data science or analytics roles - Deep expertise with Python, SQL, and data visualization tools - Expertise with experimental design, causal inference, statistical modeling, and A/B testing frameworks, particularly in high-scale technical environments - Highly effective written communication and presentation skills - A track record of translating complex data into clear, actionable insights for both technical and business stakeholders - A bias for action and ability to thrive in ambiguous, fast-moving environments where you must create clarity and drive forward progress - A passion for the company’s mission of building helpful, honest, and harmless AI - Some experience with AI/ML products, large language models, or developer tools in the AI/ML ecosystem We’re hiring across multiple pillars Applying for this role will allow you to be considered for all pillars currently hiring. You will be asked to select a preference when submitting an application. Claude Code This role is embedded with the Claude Code product team, driving data-informed decisions for Anthropic's agentic coding tool that enables developers to delegate coding tasks directly to Claude from their terminal. You'll
Expert AI & Engineering (m/f/d)
What if your next idea could outlast you? At TWT Science & Innovation, we're looking for an Expert whose curiosity knows no limits – someone who builds what others only imagine. About the Role You'll shape the frontiers of Artificial Intelligence, Machine Learning, and Simulation-based Design, Data-driven Processes, and Quantum Computing. You will collaborate closely with the R&D departments of our customers and contribute to both collaborative research initiatives and independent scientific work. You'll work with leading companies across engineering and aerospace, such as Mercedes-Benz, BMW, Porsche, Audi, Airbus, Siemens, and MTU AeroEngines , as well as with top universities like MIT, Harvard, Stanford, and ETH Zurich Design and implement advanced algorithms across AI, Data, Simulation, Processes, and Quantum Computing Lead the technical vision of interdisciplinary R&D projects Integrate emerging technologies and mentor young researchers Represent TWT at international conferences and forums PhD or Postdoc in Computer Science, Engineering, or Mathematics Deep expertise in AI, Machine Learning, or Digital Engineering Strong analytical and creative mindset Fluent in English and German Join an environment where knowledge becomes momentum. Work with global leaders and a team of explorers who believe that innovation is both science and art. JBHU1_DE
Senior Applied AI / ML Application Engineer (f/m/x...
ARE YOU PREPARED FOR THE FUTURE? AI, AI SIR! Artificial Intelligence and machine learning are at the cutting edge when it comes to shaping next-gen mobility in areas like automated driving. But it takes real intellectual leadership and expertise to identify and then harness the AI trends and technologies that will transform the way people travel for good. In this role, you will build, prototype, and deliver use cases that leverage edge device models. By understanding real user problems, model capabilities are translated into meaningful product experiences that make the voice assistant and in‑car intelligence truly useful. Together with the model engineering product team, you will be a key contributor to our product: the BMW Intelligent Personal Assistant. You will integrate models that run on edge devices within the infotainment domain. Furthermore, you will prototype & build new GenAI experiences, turning ideas into reality as refined features that will be launched to our customers. You will collaborate with the model engineering team to respect latency, memory, and performance budgets in edge devices and in the cloud. You will work closely with staff engineers to shape our model landscape and tooling infrastructure to deliver customer value. Additionally, you will build evaluation tools for real-world interaction quality, robustness, and failure modes. You will partner with our UX teams to refine design, fallback flows, and safe error handling. University degree in Computer Science, Engineering, or a related field. At least five years of proven experience in application engineering with a track record of delivering for a large customer base. Strong engineering experience integrating GenAI & ML models to serve delightful use cases. Solid Python and Java/Kotlin experience. You know how to debug ambiguous GenAI failures and improve behavior through iteration. You are hands-on with a bias for action. You enjoy working on complex problems in an international in-house team of experts. You are comfortable working with constrained, offline, edge-device models and designing around their limitations. Challenging projects with which we are shaping the mobility of tomorrow together. Wide range of personal and professional development opportunities. Attractive, fair and performance-related remuneration. High level of job security. Annual special payments such as vacation pay, Christmas bonus, and profit sharing. Flexible working hours including 6 weeks annual leave and overtime compensation. Discounted BMW & MINI conditions. Many other benefits at bmw.jobs/benefits Start date: from now on Type of employment: unlimited Working hours: FULLTIME At the BMW Group, we place great importance on equal treatment and equal opportunities. Our recruiting decisions are based on the personality, experience, and skills of the applicants. Learn more here. JBHU1_DE
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