Cohere에서 대규모 언어 모델(LLM) 학습 프레임워크를 설계하고 운영할 시니어 ML 시스템 엔지니어를 찾습니다. 분산 학습 시스템 및 HPC 인프라에 대한 깊은 이해와 JAX 또는 관련 프레임워크 활용 능력이 필수입니다. 수천 개의 GPU 환경에서 모델 학습 효율을 극대화하고 개발자 생산성을 높이는 도구를 구축하는 핵심 역할을 수행하게 됩니다.
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.
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.
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.
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).
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.
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.
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.
Cohere is remote-friendly. We have offices in Toronto, San Francisco, New York City, London, Paris, Montreal, and more coming soon.
For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.
For those not near an office: a co-working benefit so you can work alongside others in your city.
Everyone receives a $500 home office stipend to set up your workspace properly.
If any of the above doesn’t line up exactly with your experience, we still encourage you to apply.
We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.
We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.
