Togetherai

About the role As a Forward Deployed Engineer (FDE) focused on Post-Training, you will be a hands-on technical partner to our most strategic customers: AI teams using fine-tuning and reinforcement learning to build differentiated models on open-source foundations. FDE does not replace our Solutions Architects. You will work alongside them as the deep-domain specialist in post-training, guiding customers through data strategy, algorithm selection, experiment design, and evaluation, and sizing the compute behind it. You’ll make sure we can deliver on our post-training POCs, drive adoption of our fine-tuning and RL platform, and turn customer goals into measurable gains in model quality. Your work directly affects customer success, company growth, and the hardening of our core platform. Responsibilities - Data Strategy: Help customers define and execute their data strategy for model post-training, diagnose data quality issues that limit model performance outcomes, and provide recommendations for data generation for post-training scale - Algorithm Selection: Recommend the right post-training approach for each use case - Experiment Design & Hyperparameter Tuning: Design well-controlled experiments and tracking methods; diagnose training instabilities, reward hacking, and regressions. - Evaluation: Build evaluation frameworks with customers, including task-specific benchmarks, LLM-as-judge pipelines, and held-out test sets. Make sure gains are reproducibly aligned with production success metrics. - Model Selection: Guide base model selection methodology ahead of post-training efforts and baseline quality evaluations to guide post-training objectives. Interest in self-directed research of newly released open source models, particularly as they relate to post-training. - RL Workload Sizing: Scope and size RL and fine-tuning workloads, partner with sales and capacity teams to shape deals and capacity forecasts. - Strategic Customer Alignment: Serve as the primary post-training technical point of contact for aligned strategic accounts. Lead technical execution from scoping to production and make sure we hit critical milestones. - Product & Research Feedback Loop: Shape our fine-tuning and RL product roadmap with insights from the field. Contribute code, recipes, and reference implementations where needed, and drive early adoption of new features and research with strategic logos. Requirements - Experience: 5+ years in a technical role in ML engineering, Applied AI, Forward Deployed Engineering, or Solutions Architect, with recent hands-on experience post-training LLMs. - Post-Training Depth: Expert-level, hands-on experience with fine-tuning and/or RL for LLMs, including SFT, LoRA, DPO, and RL (GRPO, PPO, etc).. - Experimental Rigor: Strong track record designing experiments, running ablations, and building evaluations that separate real improvement from noise. - Data Intuition: Proven ability to build and iterate on training datasets - Model Landscape Awareness: Broad knowledge of state-of-the-art open-source models and strong judgment on which base model to post-train for a given customer requirements - Coding Proficiency: Strong Python skills; comfortable working in production environments. - Experience leading complex, customer-facing technical projects , with exceptional project management and communication skills. Able to work in a highly X-functional environment. About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Compensation We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is: $270,000 - $300,000 OTE + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Application Policy Please read each role carefully and apply to the position best suited to you. Candidates are limited to 6 applications within a 90-day period, and applications beyond that limit will be automatically rejected. You may apply to additional roles 90 days after your last application. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our Privacy Policy at https://www.together.ai/privacy

About the Role The Speculative Decoding team at Together AI builds the draft models and algorithms that let large language models generate faster without changing their outputs. Our speculators run in production on frontier open models such as DeepSeek, Kimi and GLM. Our work sits at the intersection of model training, inference algorithms and serving systems, and we co-design all three to lower the cost and latency of generation. As a research intern, you will dive into open questions in speculative decoding: new drafter architectures and verification schemes, knowledge distillation and training recipes that align draft and target models, and drafters that adapt as traffic shifts. You will train and fine-tune draft models at scale on dedicated GPU clusters, and test your ideas against realistic serving workloads, including long-context and agentic traffic. Projects aim to answer important research questions in speculative decoding, enabling faster generation on frontier models, drafters that keep pace with changing workloads, and robust, reproducible evaluation under real traffic. Past Research Speculative decoding research by Together researchers, interns and academic collaborators includes: - Speculative Speculative Decoding (ICLR 2026) - When RL Meets Adaptive Speculative Training: A Unified Training-Serving System (ICML 2026) - Beat the Long Tail: Distribution-Aware Speculative Decoding for RL Training (MLSys 2026) Responsibilities - Research and prototype new efficient speculative decoding algorithms and draft model architectures - Train, fine-tune and distill draft models, and design rigorous experiments to validate hypotheses. - Implement optimized version for serving engines to show efficacy in real world workloads - Communicate the plans, progress, and results of projects to the broader team - Share findings through technical write-ups and blog posts, and help bring successful ideas into production Requirements - Currently pursuing a Master's or Ph.D. degree in Computer Science, Electrical Engineering, or a related field - Strong knowledge of Machine Learning and Deep Learning fundamentals - Hands-on experience training or fine-tuning large models with distributed training frameworks (e.g., Megatron-LM, FSDP) - Familiarity with modern model architectures, such as Mixture-of-Experts and hybrid attention Preferred Qualifications - Prior research experience in speculative decoding, efficient inference, or efficient machine learning - Publications at leading conferences in machine learning or systems (e.g., NeurIPS, ICML, ICLR, MLSys) - Experience with knowledge distillation, post-training, or reinforcement learning - Familiarity with LLM serving engines (e.g., vLLM, SGLang, TensorRT-LLM) - Contributions to open-source machine learning projects About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Internship Program Details Our internship program runs 12 to 14 weeks, giving you the opportunity to work alongside industry-leading engineers and researchers across multiple teams. This cohort's internship dates span May 17th to August 6th or June 14th to September 3rd. Compensation We offer competitive compensation, housing stipends, and other competitive benefits. The estimated US hourly rate for this role is $58 - $70 an hour. Our hourly rates are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Application Policy Please read each role carefully and apply to the position best suited to you. Candidates are limited to 6 applications within a 90-day period, and applications beyond that limit will be automatically rejected. You may apply to additional roles 90 days after your last application. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. For more information about our privacy policy, please visit: https://www.together.ai/privacy

About the Role We're looking for a software engineer, reliability-minded but still a builder, to own what happens in production to the Kubernetes-native control plane that provisions and runs our GPU inference fleet. The platform team builds the manifest-driven API and controllers that let the inference team declare what they need, a cluster, a model deployment, a capacity change, and get it reconciled underneath. Your job is to keep that promise true in the real world: triaging InfiniBand issues when prefill and decode nodes stall exchanging KV cache over RDMA, diagnosing hero-model and LoRA-serving faults that don't show up in generic health checks, and extending the automated self-healing system so each new failure mode becomes a permanent fix instead of a recurring page. The core value we're after is keeping the decoupling real even when things break, and keeping the fleet efficient without that efficiency becoming a new source of instability. You'll own SLOs and error budgets for inference serving, lead incident response, and turn what postmortems teach you into actual changes in the controllers and automation, not just updated runbooks. You'll also work the reliability side of capacity efficiency, since a mistuned defragmentation or bin-packing pass is itself a production risk. Strong candidates have hands-on production-reliability experience on large-scale distributed or GPU-serving systems, are comfortable troubleshooting InfiniBand/RDMA and Kubernetes controller/CRD-based platforms, and think about GPU infrastructure, and the tools that keep it reliable, as software to be engineered. A product mindset applies: the on-call experience, the clarity of an alert, and the judgment built into an automated remediation are things you ship and iterate on. Responsibilities - Operate the provisioning state machine in production: own the health of the host lifecycle pipeline, discovery, inference bring-up, GPU driver/CUDA validation, health checks, decommission/RMA, triaging stuck or failed state transitions at fleet scale and feeding real failure patterns back into how the states and transitions are designed. - Triage and extend automated self-healing: when the node-scanning and remediation platform misses something, a new failure mode, an InfiniBand issue in disaggregated prefill/decode serving, a GPU Xid variant, a hero-model or LoRA-specific fault, diagnose it live, drive the fix to production, and turn it into a new detection/remediation plugin so it's automatic the next time. - Own end-to-end reliability of the pipeline: hold the SLOs and error budgets for provisioning and fleet health, lead incident response when idempotency, retry, rollback, or drift assumptions break down in practice, and drive the systemic fix, not just the hotfix. - Partner with the inference/ML platform team from the production side: be the one who knows how real cluster topologies, interconnects, and scheduling constraints actually fail in the field, and feed those failure modes back as first-class requirements into the platform's abstractions. - Work alongside machine learning researchers, engineers, and product managers to bring our latest technologies into production, enabling advanced research through awesome engineering. - Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our model inference stack. - Build tools to give us visibility into our bottlenecks and sources of instability, then design and implement solutions to address the highest-priority issues. - Lead reliability cross-functionally: build coalition across infra, ML platform, and product teams around shared reliability priorities, manage stakeholders through incidents and roadmap trade-offs, and represent the reliability point of view in planning so it shapes decisions before they ship, not after an outage. Requirements - Experience operating a provisioning or host-lifecycle pipeline in production, comfortable troubleshooting stuck or failed state transitions (discovery, bring-up, GPU driver/CUDA validation, health checks, decommission/RMA) at fleet scale, and translating real failure patterns back into improvements to the state machine’s design. - Deep experience owning production reliability for large-scale distributed systems; GPU/ML-serving infrastructure experience is a strong plus. - Hands-on experience troubleshooting InfiniBand/RDMA networking issues, ideally in a multi-node training or disaggregated-inference context — comfortable reading IB port counters and subnet manager logs, not just application metrics. - Familiarity with the operational realities of multi-tenant model serving (LoRA/adapter-based serving) and with running differentiated SLOs across model pools of different priority. - A demonstrated track record of eliminating operational toil through automation and tooling you built yourself, not just process or documentation. - Hands-on experience with Kubernetes controller/CRD patterns, reconciliation loops, and node-level health/remediation systems. - Comfort with event-driven architectures, backpressure/circuit-breaking design, and treating observability (metrics, logs, traces) as something built into a system, not added after an incident. - A software-engineering mindset first: you write and ship production code, and you think about GPU infrastructure as software to be engineered, not a fleet to be manually operated. About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Compensation We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $160,000 - $280,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Application Policy Please read each role carefully and apply to the position best suited to you. Candidates are limited to 6 applications within a 90-day period, and applications beyond that limit will be automatically rejected. You may apply to additional roles 90 days after your last application. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy .

About the Role Together AI is building the AI Native Cloud, an end-to-end platform for the full generative AI lifecycle, combining the fastest LLM inference engine with state-of-the-art GPU cloud infrastructure. The Together Cloud team builds the Together GPU Clusters flagship IaaS product that provides high-performance, AI-ready GPU clusters through a self-serve cloud console, along with the virtualized infrastructure layer powering Together's inference, RL, and fine-tuning products. As a Staff Software Engineer focusing on AI Compute in the Together Cloud org, you will set technical direction for and build major components of the next generation AI cloud platform – a highly available, global cloud infrastructure with cutting-edge virtualization of the latest ML hardware: GB300s/VRs, BlueField DPUs, InfiniBand and dual/quad-plane RoCEv2 fabrics. That virtualized computing platform powers our own SaaS products – inference, RL, and fine-tuning – and serves external cloud customers through self-serve offerings such as on-demand/reserved Kubernetes/Slurm clusters, across dozens of data centers and hundreds of thousands of GPUs. This is an architect-and-build role. Fully automated bootstrapping of GPU data centers, high-performance virtualization of GPU compute and DC networking without compromising isolation or portability, and fault-tolerant decentralized control planes — you'll set the architecture for these across our global and in-DC services, and be a key owner of the hardest parts, in the code as well as the design. Your designs will span the IaaS layer of a greenfield Vera Rubin data center up to the global management plane that schedules capacity across all of them. At this level the job is as much leverage as code: the standards you set and the engineers you grow decide how fast the rest of Together Cloud ships. Responsibilities - Own the GPU and network virtualization stack: the hypervisor, kernel, and SDN work that keeps GPU compute and DC networking high-performance, portable, and strongly isolated across heterogeneous hardware. - Own the in-DC IaaS layer: architect and roadmap the services, Kubernetes operators, and libraries that provision and manage compute, storage, and networks in our data centers — VMs, parallel filesystems, VPCs, and InfiniBand partitions; lead its build-out for a new Vera Rubin data center with thousands of GPUs, from hardware bring-up to customer-facing API. - Design the GPU scheduling and global management plane: the distributed control plane behind on-demand and reserved clusters across dozens of data centers, including the systems that scale per-cluster limits and automate the onboarding of new capacity. - Architect monitoring and automated remediation for fault tolerance: the strategy for automated detection, isolation, and recovery of failed nodes that keeps distributed pretraining and large-scale inference running through hardware failures. - Set technical direction across teams: lead design reviews, resolve cross-cutting architectural disagreements, unblock cross-team dependencies and integration risks, and define the standards other engineers build against — measured in cluster reliability, time-to-first-GPU on new capacity, and quality at scale. - Grow the team: mentor senior and junior engineers, deepen the team's expertise in virtualization, DC networking, and GPU infrastructure, and help raise the hiring bar for Together Cloud. - Set the engineering bar: create the testing frameworks, tools, and developer documentation that make our systems robust and usable by other teams, and shape the core, open-source Together AI platform. To be successful you'll need to be deeply technical and an excellent communicator — expert software development fundamentals, deep systems knowledge and troubleshooting instincts, and the leadership and diplomacy skills to align teams that don't report to you. Much of this work starts ambiguous, and we expect you to define the scope yourself and drive it to production. Requirements - 7+ years of professional software development experience, with expert-level proficiency in at least one backend language (Golang desired), writing high-performance, well-tested, production-quality code. - Track record of owning the architecture of large distributed systems from blank page to production at scale, including the judgment calls that could not be reversed cheaply. - Deep experience building and operating globally distributed, high-performance microservice architectures across one or more cloud providers (AWS, Azure, GCP). - Expert systems knowledge across compute, networking, and storage — including concurrency, memory management, performant I/O, and scale at a global level. - Demonstrated technical leadership beyond your own commits: mentoring senior engineers, leading design reviews, and driving alignment across teams that do not report to you. - Excellent communication and diplomacy skills — able to write design docs that settle arguments, and to work effectively with technical and non-technical stakeholders. - Experience building and operating reliable, customer-facing production systems at scale, and owning the infrastructure automation (Terraform, Ansible), observability (Prometheus, Grafana), and CI/CD (GitHub Actions, ArgoCD) that keep them healthy. Preferred Qualifications (not must haves) - Deep Kubernetes internals experience, such as implementing non-trivial Kubernetes operators, device/storage/network plugins, custom schedulers, or patches to Kubernetes itself - Deep experience with VMs/hypervisors, such as QEMU/KVM, cloud-hypervisor, VFIO, virtio, PCIE passthrough, Kubevirt, SR-IOV - Deep experience with DC networking tech + solutions, such as VLAN, VXLAN, VPN, VPC, OVS/OVN - Experience with Cluster API or similar - Experience working on high-performance compute, networking, and/or storage - Experience virtualizing GPUs and/or InfiniBand - Experience building IaaS or PaaS systems at scale - Experience with DPUs/SmartNICs - GPU programming, NCCL, CUDA knowledge About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Application Policy Please read each role carefully and apply to the position best suited to you. Candidates are limited to 6 applications within a 90-day period, and applications beyond that limit will be automatically rejected. You may apply to additional roles 90 days after your last application. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy

About the Role The Speculative Decoding team at Together AI builds the draft models and algorithms that let large language models generate faster without changing their outputs. Our speculators run in production on frontier open models such as DeepSeek, Kimi and GLM. Our work sits at the intersection of model training, inference algorithms and serving systems, and we co-design all three to lower the cost and latency of generation. As a research intern, you will dive into open questions in speculative decoding: new drafter architectures and verification schemes, knowledge distillation and training recipes that align draft and target models, and drafters that adapt as traffic shifts. You will train and fine-tune draft models at scale on dedicated GPU clusters, and test your ideas against realistic serving workloads, including long-context and agentic traffic. Projects aim to answer important research questions in speculative decoding, enabling faster generation on frontier models, drafters that keep pace with changing workloads, and robust, reproducible evaluation under real traffic. Past Research Speculative decoding research by Together researchers, interns and academic collaborators includes: - Speculative Speculative Decoding (ICLR 2026) - When RL Meets Adaptive Speculative Training: A Unified Training-Serving System (ICML 2026) - Beat the Long Tail: Distribution-Aware Speculative Decoding for RL Training (MLSys 2026) Responsibilities - Research and prototype new efficient speculative decoding algorithms and draft model architectures - Train, fine-tune and distill draft models, and design rigorous experiments to validate hypotheses. - Implement optimized version for serving engines to show efficacy in real world workloads - Communicate the plans, progress, and results of projects to the broader team - Share findings through technical write-ups and blog posts, and help bring successful ideas into production Requirements - Currently pursuing a Master's or Ph.D. degree in Computer Science, Electrical Engineering, or a related field - Strong knowledge of Machine Learning and Deep Learning fundamentals - Hands-on experience training or fine-tuning large models with distributed training frameworks (e.g., Megatron-LM, FSDP) - Familiarity with modern model architectures, such as Mixture-of-Experts and hybrid attention Preferred Qualifications - Prior research experience in speculative decoding, efficient inference, or efficient machine learning - Publications at leading conferences in machine learning or systems (e.g., NeurIPS, ICML, ICLR, MLSys) - Experience with knowledge distillation, post-training, or reinforcement learning - Familiarity with LLM serving engines (e.g., vLLM, SGLang, TensorRT-LLM) - Contributions to open-source machine learning projects About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Internship Program Details Our internship program runs 12 to 14 weeks, giving you the opportunity to work alongside industry-leading engineers and researchers across multiple teams. This cohort's internship dates span January 4th to April 9th. Compensation We offer competitive compensation, housing stipends, and other competitive benefits. The estimated US hourly rate for this role is $58 - $70 an hour. Our hourly rates are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Application Policy Please read each role carefully and apply to the position best suited to you. Candidates are limited to 6 applications within a 90-day period, and applications beyond that limit will be automatically rejected. You may apply to additional roles 90 days after your last application. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. For more information about our privacy policy, please visit: https://www.together.ai/privacy

About The Role We're looking for a software engineer, reliability-minded but still a builder, to own what happens in production to the Kubernetes-native control plane that provisions and runs our GPU inference fleet. The platform team builds the manifest-driven API and controllers that let the inference team declare what they need, a cluster, a model deployment, a capacity change, and get it reconciled underneath. Your job is to keep that promise true in the real world: triaging InfiniBand issues when prefill and decode nodes stall exchanging KV cache over RDMA, diagnosing hero-model and LoRA-serving faults that don't show up in generic health checks, and extending the automated self-healing system so each new failure mode becomes a permanent fix instead of a recurring page. The core value we're after is keeping the decoupling real even when things break, and keeping the fleet efficient without that efficiency becoming a new source of instability. You'll own SLOs and error budgets for inference serving, lead incident response, and turn what postmortems teach you into actual changes in the controllers and automation, not just updated runbooks. You'll also work the reliability side of capacity efficiency, since a mistuned defragmentation or bin-packing pass is itself a production risk. Strong candidates have hands-on production-reliability experience on large-scale distributed or GPU-serving systems, are comfortable troubleshooting InfiniBand/RDMA and Kubernetes controller/CRD-based platforms, and think about GPU infrastructure, and the tools that keep it reliable, as software to be engineered. A product mindset applies: the on-call experience, the clarity of an alert, and the judgment built into an automated remediation are things you ship and iterate on. Responsibilities - Operate the provisioning state machine in production: own the health of the host lifecycle pipeline, discovery, inference bring-up, GPU driver/CUDA validation, health checks, decommission/RMA, triaging stuck or failed state transitions at fleet scale and feeding real failure patterns back into how the states and transitions are designed. - Triage and extend automated self-healing: when the node-scanning and remediation platform misses something, a new failure mode, an InfiniBand issue in disaggregated prefill/decode serving, a GPU Xid variant, a hero-model or LoRA-specific fault, diagnose it live, drive the fix to production, and turn it into a new detection/remediation plugin so it's automatic the next time. - Own end-to-end reliability of the pipeline: hold the SLOs and error budgets for provisioning and fleet health, lead incident response when idempotency, retry, rollback, or drift assumptions break down in practice, and drive the systemic fix, not just the hotfix. - Partner with the inference/ML platform team from the production side: be the one who knows how real cluster topologies, interconnects, and scheduling constraints actually fail in the field, and feed those failure modes back as first-class requirements into the platform's abstractions. - Work alongside machine learning researchers, engineers, and product managers to bring our latest technologies into production, enabling advanced research through awesome engineering. - Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our model inference stack. - Build tools to give us visibility into our bottlenecks and sources of instability, then design and implement solutions to address the highest-priority issues. - Lead reliability cross-functionally: build coalition across infra, ML platform, and product teams around shared reliability priorities, manage stakeholders through incidents and roadmap trade-offs, and represent the reliability point of view in planning so it shapes decisions before they ship, not after an outage. Requirements - Experience operating a provisioning or host-lifecycle pipeline in production, comfortable troubleshooting stuck or failed state transitions (discovery, bring-up, GPU driver/CUDA validation, health checks, decommission/RMA) at fleet scale, and translating real failure patterns back into improvements to the state machine’s design. - Deep experience owning production reliability for large-scale distributed systems; GPU/ML-serving infrastructure experience is a strong plus. - Hands-on experience troubleshooting InfiniBand/RDMA networking issues, ideally in a multi-node training or disaggregated-inference context — comfortable reading IB port counters and subnet manager logs, not just application metrics. - Familiarity with the operational realities of multi-tenant model serving (LoRA/adapter-based serving) and with running differentiated SLOs across model pools of different priority. - A demonstrated track record of eliminating operational toil through automation and tooling you built yourself, not just process or documentation. - Hands-on experience with Kubernetes controller/CRD patterns, reconciliation loops, and node-level health/remediation systems. - Comfort with event-driven architectures, backpressure/circuit-breaking design, and treating observability (metrics, logs, traces) as something built into a system, not added after an incident. - A software-engineering mindset first: you write and ship production code, and you think about GPU infrastructure as software to be engineered, not a fleet to be manually operated. About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Compensation We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $250,000 - $320,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy .

About the Role We're hiring the best people in the market, and we need a sourcer who can help us find and win them. As a Recruiting Sourcer, you'll build and nurture the top of the funnel for our business-side hiring across Go-to-Market, Infrastructure Strategy, and G&A. You'll work with senior recruiting partners and business leaders on critical, hard-to-fill searches. Your first priority will be clearing our open searches by finding and engaging exceptional, mostly passive candidates. Over time, you'll build a warm pipeline of top-tier talent and a repeatable sourcing playbook that helps us scale. This is a chance to tell Together AI's story to people who are already doing great work elsewhere, and to see that work turn into real hires. Responsibilities - Source, engage, and nurture top-tier passive candidates across GTM, Infrastructure Strategy, and G&A, including Enterprise Sales, Solutions Engineering, Sales leadership, Marketing, Customer Success, Director-level G&A, and Strategy and Infrastructure roles at the intersection of finance and AI compute. - Partner with hiring managers and recruiters to calibrate on roles and ideal candidate profiles before sourcing - Write personalized outreach that conveys Together AI's story and resonates with each candidate - Proactively build pipelines for current and future roles, without waiting to be assigned - Build and maintain long-term relationships with top-tier talent - Track key sourcing metrics and use data to refine strategy and improve results - Develop repeatable sourcing systems and a playbook to scale as hiring grows - Work within our sourcing and recruiting stack, including GEM, LinkedIn Recruiter, Noon AI, and Greenhouse - Adapt quickly as priorities shift and move between functions with ease Requirements - 5+ years of sourcing experience (strong candidates with 3-4 years and exceptional results on hard-to-fill roles are also encouraged to apply) - Track record sourcing senior passive talent for critical, hard-to-fill roles across a range of functions and departments - Strong sourcing, mapping, and outreach skills, with the ability to craft compelling, personalized messaging - Experience in a startup or hyper-growth environment at some point in your career - Experience building sourcing systems, identifying key metrics, and making data-driven decisions - Strong communication skills and the ability to build trust with stakeholders and candidates - Self-directed and operationally agile, comfortable with shifting priorities - Genuine interest in AI, with some knowledge of the space or a willingness to learn - Experience with sourcing tools like GEM, LinkedIn Recruiter, and Noon AI is a plus; familiarity with Greenhouse or a similar ATS, or the ability to adopt new tools quickly If you don't meet every requirement but believe you'd be a great fit, we'd still love to hear from you. About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Compensation We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $ 125 -190K + bonus program + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunities Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy .

About the Role Together AI is looking for a Field Marketing Manager to build and scale our account-based field and in-person community programs across priority markets. This role will be responsible for developing the strategy, audience, format, and execution of high-impact field programs designed to engage priority accounts, accelerate pipeline, deepen customer relationships, and build authentic technical community. You’ll own programs end-to-end, from identifying the right accounts and developing the event concept to driving attendance, managing the attendee experience, partnering with Sales on follow-up, and measuring business impact. This is a highly cross-functional role for someone who can pair strong strategic judgment with exceptional execution. You should be just as comfortable building an ABM event strategy with Sales as you are selecting a venue, shaping an agenda, managing a vendor, or reviewing the attendee list the day before an event. Responsibilities ABM & Account-Based Events - Develop and execute an ABM event strategy aligned to priority segments, accounts, pipeline goals, and sales priorities. - Develop repeatable account-based field programs for priority accounts and segments, including dinners, roundtables, customer gatherings, and other targeted regional programs. - Partner closely with Sales to identify the right accounts, stakeholders, and moments in the buying cycle for each program. - Own account targeting, invitation strategy, registration goals, attendance conversion, and post-event follow-up plans. - Ensure every program has a clear business objective and defined next step for participating accounts. - Evaluate program performance based on account engagement, pipeline creation and acceleration, POCs, opportunities, and other meaningful business outcomes. Community Programs - Build and scale Together AI’s community event strategy across key markets. - Develop meetups, technical gatherings, workshops, partner programs, and other formats that bring together developers, researchers, AI practitioners, customers, and ecosystem partners. - Partner with DevRel, Research, Product, Engineering, and other technical teams to develop programming that is genuinely useful and relevant to the audience. - Identify opportunities to partner with customers, investors, technology partners, startups, and community organizations to extend reach and credibility. - Build repeatable playbooks while ensuring programs still feel authentic to the local community. End-to-End Program Ownership - Own programs from strategy through execution, including concept development, audience strategy, venue sourcing, budget, vendors, registration, attendee communications, run-of-show, onsite execution, and follow-up. - Manage multiple programs simultaneously while maintaining a high bar for quality and attendee experience. - Partner with the broader Events team to support select third-party conferences and industry moments where field, community, or account-based activation can extend the impact of Together AI’s presence. - Partner with Creative and Brand on event identity, environmental design, signage, swag, and other experience touchpoints. - Develop clear event briefs, timelines, budgets, staffing plans, and cross-functional workbacks. - Own vendor relationships and negotiate contracts to maximize quality and efficiency. - Travel as needed to support programs onsite. Measurement & Optimization - Partner with Marketing Ops, Demand Gen, and Sales to ensure programs are properly tracked through Salesforce and our event technology stack. - Build consistent pre- and post-event reporting that shows who attended, account engagement, pipeline impact, and follow-up status. - Conduct post-event retrospectives and turn learnings into stronger future programs. - Use data and qualitative feedback to recommend where we should invest more, change approach, or stop investing. Requirements - 5–7+ years of experience in field marketing, event marketing, ABM, or a related B2B technology marketing role. - Experience developing and executing account-based programs in close partnership with Sales. - Strong understanding of how events can support pipeline creation, acceleration, customer expansion, and relationship development. - Experience creating community or experiential programs for technical audiences. - Strong project management skills with the ability to independently manage multiple complex programs at once. - Exceptional attention to detail and a high bar for attendee experience. - Strong written and verbal communication skills. - Ability to work effectively across Sales, Demand Gen, Product Marketing, DevRel, Research, Creative, Partnerships, and executive stakeholders. - Comfortable using Salesforce and event/marketing technology to track performance and business impact. - Strong judgment and the ability to prioritize opportunities based on expected impact, not simply volume of requests. - High agency and comfort operating in a fast-moving environment with ambiguity. - Ability to travel up to 30–40% based on event schedules, including international travel. About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Compensation We offer competitive compensation, startup equity, health insurance, and other benefits. The US base salary range for this full-time position is: $170,000 - $195,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our Privacy Policy at https://www.together.ai/privacy

About the Role As the Sr. Partnerships Manager for US Model Labs, you will own Together AI's relationships with the most influential model builders in the United States: frontier labs, the AI research organizations inside the hyperscalers and major platform companies, chipmakers with their own model programs, and the well-funded independent labs shaping the open-weights landscape. These are the partners whose releases move the market, and your job is to make sure Together AI is the platform they launch on, build with, and route developers to. This is a portfolio of a small number of very large, very complex accounts. Unlike a broad ecosystem role, success here is measured in depth rather than breadth: multi-threaded relationships across research, product, legal, and business teams; deals that often bundle model distribution with compute, co-marketing, and joint go-to-market; and the trust required to get early access to models before they ship. You will sit at the intersection of Product, Engineering, Finance, and Marketing, and you will frequently be the person in the room representing Together AI to organizations many times our size. You are a strategic deal-maker who is comfortable in long, ambiguous cycles with sophisticated counterparties, and who can turn a single relationship into a durable, expanding partnership. Responsibilities - Own the US lab portfolio: Build and execute the partnership strategy for a defined set of major US model labs, including account plans, executive engagement, and a clear view of each lab's roadmap and where Together AI fits in it. - Land flagship launches: Secure day-zero (or day-one) availability of the most anticipated model releases on the Together platform, coordinating pre-release access, technical bring-up, legal, and launch marketing so we ship alongside the lab. - Multi-thread across large organizations: Navigate research teams, product leads, developer relations, alliances and partner programs, legal, and executive sponsors to build support for Together AI at every level of the partner org. - Structure strategic deals: Lead negotiation of distribution, licensing, revenue-share, compute, and co-marketing agreements, including non-standard structures that combine several of these. Handle complex IP, usage-rights, and exclusivity discussions with counterparties that have deep in-house legal and BD teams. - Product and engineering alignment: Work with Product and Engineering to define bring-up requirements, manage NDA'd early access, and ensure new models are optimized for our inference and fine-tuning stacks ahead of launch. - Commercial strategy: Partner with Finance to build deal structures that balance aggressive growth with sustainable unit economics, and to model the revenue impact of flagship model availability. - Go-to-market and co-selling: Collaborate with Partner Marketing on joint announcements and campaigns, and work with Sales to turn lab relationships into enterprise wins through co-selling motions, joint customer engagements, and partner referral programs. - Be the voice of the labs internally: Bring market and roadmap intelligence back to Product and Leadership, and shape how Together AI positions itself relative to the labs' own platforms and their cloud partners. Requirements - Enterprise-scale partnership experience: 7–10+ years in Strategic Partnerships, Business Development, or Alliances, with a track record of closing and growing partnerships with large technology companies where the counterparty had its own BD, legal, and partner teams. - Strategic deal-maker: You have constructed complex, multi-component agreements that don't follow a standard template, and you can hold your own in IP, licensing, revenue-share, and exclusivity negotiations with sophisticated counterparties. - High EQ and executive presence: You build trust quickly, manage long relationships through changes in personnel and priorities, and are credible in front of senior leaders at partner organizations. - Technical fluency: While you don't need to be an ML researcher, you should be able to discuss model architectures, context windows, licensing terms, and inference and fine-tuning requirements with both partners and our internal teams. - AI-native mindset: You work at pace with a relentless sense of urgency, and you prefer "done and iterating" over "perfect and stagnant," even while managing accounts that move on their own timelines. Bonus Points - Direct experience working at, or partnering with, a frontier lab, a hyperscaler AI organization, or a major open-weights model builder. - Experience with hyperscaler or cloud marketplace partner programs (e.g., alliances, co-sell, or ISV programs). - Experience in co-selling motions where partnerships directly drove revenue. - Background at a high-growth API, cloud infrastructure, or developer platform company managing complex ecosystems. About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Compensation We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $ 300 - 350K OTE + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy .

About the Role This is not a traditional sourcing role. We're looking for someone who was an engineer first and wants to put that technical depth to work finding and winning the best engineers in the market. You will own proactive outbound sourcing only the top 1% technical talent in the Bay Area and our other hubs identifying them, reaching them, and judging their work on its merits before anyone else does. You'll build and run outreach campaigns, keep pipelines full for specific engineering roles, and bring a steady, systematic hustle to it. What sets this role apart is that you can evaluate technical talent. You can read a GitHub profile and see the signal in it. You can run a genuinely technical first call, get into the nuts and bolts of what someone built and their level of ownership, and tell the difference between top-tier and the rest after a single conversation. You'll report directly to the hiring manager on a tight-knit team of six, with visibility and room to grow into ownership of how we source and evaluate engineering talent. We'll teach you the recruiting craft - the technical judgment is what you bring. Responsibilities - Build and maintain pipelines of top-1% engineering talent for specific open roles - Design and run proactive outreach campaigns to elite engineers - persistent, high-volume, and well-written - Review GitHub profiles and other technical signals to identify genuinely strong candidates - Run technical first calls that go deep on what a candidate actually built - Screen for real quality, filtering aggressively so that only the best talent reaches the hiring team - Partner closely with the hiring manager and recruiter to calibrate on what "great" looks like for each role - Help shape Together AI's approach to technical sourcing as the function grows Requirements - A hands-on engineering background - you've worked as an engineer of some kind. A CS degree is a strong plus. - The ability to read a GitHub profile and glean real signal about someone's depth and quality - Enough current technical fluency to run a deep, credible first call with a strong engineer - Persistence, hustle, and resilience - you're energized by outbound and unfazed by rejection - Strong written communication that gets busy engineers to reply - Based in San Francisco - Recruiting or sourcing experience is a bonus, not a requirement - we'll teach you that part. About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Compensation We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $125,000 - $190,000K + equity + benefits + incentive bonus. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy .

About The Role Together AI is the AI native cloud, which has been built bottoms up to cater to the demand for the new generation of AI applications and agents. The company has seen tremendous growth with 10X growth year on year and has over a million developers. As we continue to drive product innovation, we are also investing deeply in our GTM. This role will lead partner marketing at Together AI. They will define our partner marketing strategy and own the execution, in collaboration with the GTM leadership. This role will report into the head of product and solution marketing and will have the opportunity to own and independently build the partner marketing function at one of the most innovative companies at the forefront of AI. Responsibilities - Create and execute partner marketing strategy, specially for NVIDIA and cloud partners – in collaboration with GTM leadership - Own core messaging for partners, working closely with product marketing to build those - Be the marketing POC (point of contact) for partners and run regular rhythms of business to drive co-marketing efforts - Collaborate with demand gen and field marketing to run partner marketing campaigns - Outline and track metrics to measure our success in the partner space - Experiment and try new ideas that can help us scale further the ROI for both partners and Together aI Requirements - 5+ years of partner marketing experience, preferably working with cloud hyperscalers (Azure, AWS, GCP) and/or NVIDIA - Proven track record in creating and implementing high impact partner campaigns for B2B businesses, preferably in AI or Software Platform (PaaS) - Strong ownership mindset and ability to build something from ground up - Experience with scaling organizations through partner GTM - Strong analytical skills with a data-driven approach to decision-making and reporting - Bachelors degree required, preferred if in engineering About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, reinforcement learning to create specialized models, and pre-training for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves over 400 trillion tokens a month. Compensation We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $220-250K + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. This is a hybrid role based in the Bay Area. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy

About the Role As a Hardware Security Engineer, you’ll contribute to building open, transparent, and secure AI systems. You’ll have the opportunity to work on projects that safeguard corporate assets by implementing and operating preventative and reactive security controls while collaborating with teams and stakeholders across the company. We’re looking for an intern that is interested in building AI-assisted security apps. Responsibilities - Design, prototype, and implement security solutions for AI infrastructure across hardware, firmware, operating systems, and cloud services, including BMCs, TPMs, secure boot chains, networking interfaces, and workload identity systems. - Assess and improve the security posture of server and accelerator platforms, including firmware update processes, device identity, trusted execution, and hardware-backed cryptography. - Support the secure platform lifecycle, from device onboarding, provisioning, and inventory through configuration validation and decommissioning. - Build automation and internal tools in Python, Bash, or similar languages to collect platform telemetry, detect configuration drift, and support incident investigation. - Build AI-assisted security applications for asset classification, anomaly detection, and detection-and-response workflows. - Partner with IT and infrastructure teams to implement and enforce identity, access-control, endpoint, and privileged-access security practices. - Research and evaluate emerging technologies such as confidential computing, remote attestation, hardware roots of trust, and secure device management. - Write clean, well-documented code, technical designs, test plans, and security findings. Requirements - Actively pursuing a degree in Electrical Engineering, Computer Science, Software Engineering, or a related field expected by Summer 2027 - Foundational understanding of computer architecture, operating systems, networking, embedded systems, digital logic, or electronics - Strong programming skills with a proficiency in Python or bash - Exposure to building AI-assisted applications Preferred Qualifications - Prior security engineering experience through internships, hackathons, or projects - Coursework, projects, research, or internship experience in hardware security, embedded security, platform security, computer architecture, or secure systems design - Experience with one or more of the following: Microcontrollers, FPGA development, board bring-up, JTAG/SWD debugging, serial interfaces, SPI, I2C, UART, PCIe, or Ethernet About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Compensation We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $140,000 - $150,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy

About the Role As Director of Investor Relations at Together, you will own the company's relationships with its investors and shape how the equity capital markets understand Together's business, strategy, and long-term opportunity. Reporting to the VP of FP&A, and partnering closely with the CEO, CFO, and executive team, you will be responsible for building our investor engagement strategy and investment narrative. You will also own the core planning and reporting work that underpins our investor story: board reporting, the long-range plan, and the KPIs we use to run and describe the business. This hybrid investor relations and FP&A role will provide deep and unique insight into building and scaling an AI inference company. This is a hands-on, high-ownership role in a lean function: you will build investor relations at Together largely from the ground up. This role is well suited to someone with analytical rigor and a background in equity capital markets who follows developments in technology and AI closely. You will be responsible for deeply understanding investors' perspectives on Together as well as the broader industry, bringing strategic insights to leadership to help inform key decisions. Strong candidates pair depth in financial analysis with the ability to distill sophisticated financial and technical concepts into clear and concise investor narratives. Responsibilities Investor Relations - Serve as a key point of contact for Together's equity investors - Play a leading role in primary and secondary fundraising processes alongside the CFO and CEO, including building investor materials, running the data room and diligence process, coordinating investor meetings, and managing the process through close - Develop and sharpen Together's investment narrative, showing how our technology, business model, infrastructure footprint, and financial profile add up to a compelling long-term thesis - Build the reporting cadence and package for equity investors. Manage ongoing investor communications and requests, including regular investor reporting, ad hoc requests, and follow-ups - Advise leadership on investor sentiment, market expectations, and how key decisions are likely to be received - Develop a view of Together’s current and prospective shareholder base and how it should evolve, leading investor targeting and outreach accordingly - Prepare the CEO, CFO, and executive team for investor and board conversations by anticipating questions, sharpening key messages, and providing the supporting data - Track developments across AI, cloud infrastructure, and equity capital markets, including peer fundraises, valuations, and investor sentiment, and translate them into concise insight for leadership Planning & Reporting - Own the quarterly board reporting package, working with finance and functional leaders to present a clear, accurate picture of the business - Run the long-range plan, including the fundraise and valuation models that connect the company's capital needs to its operating plan - Help define and evolve our monthly KPI reporting, establishing the metrics leadership and investors use to understand performance and benchmark against peers and competitors Requirements - Experience in investor relations, growth equity, venture capital, equity research, investment banking, or a comparable equity capital markets role - Exceptional written and verbal communication skills, with the ability to distill complex financial and technical topics into sharp, credible messages for sophisticated investors - A deep understanding of equity capital markets and how investors evaluate high-growth, capital-intensive companies - Familiarity with, or demonstrated interest in, AI and cloud infrastructure business models, such as GPU capacity economics, usage-based revenue, and infrastructure capex - Ability to build and maintain financial models, and to translate financial statements and operating metrics into a defensible view of a business - Sound judgment handling confidential information, with the discipline to know what should be shared, when, and with whom - Comfort working directly with company executives, board members, and senior investors - A strong work ethic and self-direction, with the creativity and problem-solving instincts to tackle novel questions where there is no established playbook Nice to Have - Direct experience executing late-stage private fundraises, from either the company or the investor side - Experience with consumption-based revenue or large-scale infrastructure business models About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Compensation We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $ 265-310K + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy .

About the Role Together AI is building the AI Acceleration Cloud. We are building an end-to-end platform for the generative AI lifecycle, integrating fast, reliable inference and model-shaping services with cutting-edge AI cloud infrastructure. We're looking for an exceptional Senior GTM & Business Recruiter who can help us scale our teams while keeping the bar of excellence high. You'll own end-to-end hiring across GTM and Business functions, from first conversation through offer close, and partner with GTM leadership to translate ambitious hiring plans into real teams. This role is based out of our Headquarters in San Francisco. Responsibilities - Own full-cycle strategic recruiting across GTM and Business organizations. - Partner with executives and leaders to define hiring strategies, candidate profiles, and interview processes. - Manage the candidate journey from sourcing handoff to offer stage, ensuring a seamless and exceptional experience. - Provide market intelligence, funnel metrics, and competitive insights to shape hiring strategies and influence decisions. - Collaborate with sourcers, coordinators, and recruiters to streamline processes and deliver a consistent, brand-aligned candidate experience. - Act as a strategic thought partner to leaders, scaling from concept to execution with data-driven insights, creativity, and sound judgment. Requirements - 5+ years of business or GTM recruiting experience in high-growth tech environments. - Strong ability to build and maintain relationships with leadership and hiring teams. - You thrive in a fast-paced, ambiguous environment with rapidly shifting priorities. - Expertise in designing new interview processes and hiring strategies from the ground up. - You adapt gracefully under pressure and navigate changing priorities with ease. - Ability to do more with less, you thrive in a scrappy environment and you find creative solutions to problems About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Compensation We offer competitive compensation, startup equity, health insurance, and other benefits. The US base salary range for this full-time position is: $165,000 - $210,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our Privacy Policy at https://www.together.ai/privacy

About the Role As a Hardware Security Engineer Intern, you’ll contribute to building open, transparent, and secure AI systems. You’ll have the opportunity to work on projects that safeguard corporate assets by implementing and operating preventative and reactive security controls while collaborating with teams and stakeholders across the company. We’re looking for an intern that is interested in building AI-assisted security apps. Responsibilities - Help design, prototype, and assess security solutions for AI infrastructure across hardware, firmware, operating systems, and cloud services, working with components like BMCs, TPMs, secure boot chains, and device and workload identity systems. - Support secure platform lifecycle work such as device onboarding, provisioning, inventory, and configuration validation. - Build automation and internal tools in Python, Bash, or similar languages to collect platform telemetry, detect configuration drift, or support incident investigations. - Prototype AI-assisted security tools for tasks like asset classification, anomaly detection, or detection-and-response workflows. - Research an emerging area such as confidential computing, remote attestation, or hardware roots of trust, and share your findings with the team. - Own a well-scoped project from start to finish with mentorship from experienced engineers, delivering clean, well-documented code and presenting your results at the end of the internship. Requirements - Bachelor’s degree (or equivalent) in Electrical Engineering, Computer Science, Software Engineering, or a related field, earned or expected by Summer 2028 - Foundational understanding of computer architecture, operating systems, networking, embedded systems, digital logic, or electronics - Strong programming skills with a proficiency in Python or bash - Exposure to building AI-assisted applications Preferred Qualifications - Prior security engineering experience through internships, hackathons, or projects - Coursework, projects, research, or internship experience in hardware security, embedded security, platform security, computer architecture, or secure systems design About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Internship Program Details: Our internship program spans over twelve weeks where you’ll have the opportunity to work with industry-leading engineers building a cloud from the ground up and possibly contribute to influential open source projects. Our internship dates are May 18th to August 7th or June 15th to September 4th. Compensation We offer competitive compensation, housing stipends, and other competitive benefits. The estimated US hourly rate for this role is $58. Our hourly rates are determined by location, level, and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy