Senior ML Systems Engineer, Frameworks & Tooling

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company co-headquartered in Toronto and San Francisco, with key offices in London, New York City, Montreal, Seoul, Germany and Paris. Join us! We’re looking for a senior engineer to help build, maintain and evolve the training framework that powers our frontier-scale language models. This role sits at the intersection of large-scale training, distributed systems, and HPC infrastructure. You will design and maintain the core components that enable fast, reliable, and scalable model training — and build the tooling that connects research ideas to thousands of GPUs. If you enjoy working across the full stack of ML systems, this role gives you the opportunity and autonomy to have massive impact. What You’ll Work On - Build and own the training framework responsible for large-scale LLM training. - Design distributed training abstractions (data/tensor/pipeline parallelism, FSDP/ZeRO strategies, memory management, checkpointing). - Improve training throughput and stability on multi-node clusters (e.g., GB200/300, AMD, H200/100). - Develop and maintain tooling for monitoring, logging, debugging, and developer ergonomics. - Collaborate closely with infra teams to ensure our cluster, container environments, and hardware configurations support high-performance training. - Investigate and resolve performance bottlenecks across the ML systems stack. - Build robust systems that ensure reproducible, debuggable, large-scale runs. You Might Be a Good Fit If You Have - Strong engineering experience in large-scale distributed training or HPC systems. Deep familiarity with JAX internals, distributed training libraries, or custom kernels/fused ops. - Experience with multi-node cluster orchestration (Slurm, Ray, Kubernetes, or similar). - Comfort debugging performance issues across CUDA/NCCL, networking, IO, and data pipelines. - Experience working with containerized environments (Docker, Singularity/Apptainer). - A track record of building tools that increase developer velocity for ML teams. - Excellent judgment around trade-offs: performance vs complexity, research velocity vs maintainability. - Strong collaboration skills — you’ll work closely with infra, research, and deployment teams. Nice to Have - Experience with training LLMs or other large transformer architectures. - Contributions to ML frameworks (PyTorch, JAX, DeepSpeed, Megatron, xFormers, etc.). - Familiarity with evaluation and serving frameworks (vLLM, TensorRT-LLM, custom KV caches). - Experience with data pipeline optimization, sharded datasets, or caching strategies. - Background in performance engineering, profiling, or low-level systems. Bonus: paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP). Why Join Us - You’ll work on some of the most challenging and consequential ML systems problems today. - You’ll collaborate with a world-class team working fast and at scale. - You’ll have end-to-end ownership over critical components of the training stack. - You’ll shape the next generation of infrastructure for frontier-scale models. - You’ll build tools and systems that directly accelerate research and model quality. Sample Projects: - Build a high-performance data loading and caching pipeline. - Implement performance profiling across the ML systems stack - Develop internal metrics and monitoring for training runs. - Build reproducibility and regression testing infrastructure. - Develop a performant fault-tolerant distributed checkpointing system. Full-Time Employees at Cohere enjoy these Perks: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. How and Where We Work: - Cohere is remote-friendly. We have offices in Toronto, San Fra

Strong engineering experience in large-scale distributed training or HPC systems. Deep familiarity with JAX internals, distributed training libraries, or custom kernels/fused ops. Experience with multi-node cluster orchestration (Slurm, Ray, Kubernetes, or similar). Comfort debugging performance issues across CUDA/NCCL, networking, IO, and data pipelines. Experience working with containerized environments (Docker, Singularity/Apptainer). A track record of building tools that increase developer velocity for ML teams. Excellent judgment around trade-offs: performance vs complexity, research velocity vs maintainability. Strong collaboration skills — you’ll work closely with infra, research, and deployment teams.

Build and own the training framework responsible for large-scale LLM training. Design distributed training abstractions (data/tensor/pipeline parallelism, FSDP/ZeRO strategies, memory management, checkpointing). Improve training throughput and stability on multi-node clusters (e.g., GB200/300, AMD, H200/100). Develop and maintain tooling for monitoring, logging, debugging, and developer ergonomics. Collaborate closely with infra teams to ensure our cluster, container environments, and hardware configurations support high-performance training. Investigate and resolve performance bottlenecks across the ML systems stack. Build robust systems that ensure reproducible, debuggable, large-scale runs.