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 headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: 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 by working on projects such as: Building a high-performance data loading and caching pipeline. Implementing performance profiling across the ML systems stack Developing internal metrics and monitoring for training runs. Building reproducibility and regression testing infrastructure. Developing a performant fault-tolerant distributed checkpointing system. Key Responsibilities: 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. Qualifications: 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. Any of the following would also be good to have for this role: 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). Working Location: This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.