Member of Technical Staff (Integration/RL Team, Research Engineer) Cohere
Enterprise-grade AI solutions for business innovation and productivity.
Permanent
Paris
Fully-remote
Salary: Not specified
> 1 year
Skills we're looking for
Teamwork
Collaboration and teamwork
Kubernetes
Pytorch
Python
You'll be part of
806 employees
Artificial Intelligence / Machine Learning
Our take
Many businesses are now faced with the challenge of adopting advanced AI safely and securely. While AI tools like ChatGPT are making headlines, companies (especially those in regulated industries such as banking, telecoms, or government services) worry about privacy, compliance, and control over their sensitive data. Cohere addresses these concerns by offering AI solutions that can be trained on a company's internal data and, importantly, run on their own infrastructure rather than relying on external cloud providers. And in a world where data breaches and regulatory scrutiny are constant threats, this focus on trust and security is key. In a competitive landscape, dominated by giants like OpenAI, Google, and Elon Musk;'s xAI, Cohere is setting itself apart by targeting businesses exclusively. Its approach has earned it contracts with some big names, like Royal Bank of Canada, Fujitsu, LG, Oracle, and Dell. By focusing on reliability and enterprise adoption rather than viral consumer appeal, Cohere has carved out a strong niche for itself. This isn't going unnoticed either, as the company recently raised $500M, boosting its valuation to $6.8B, and brought on board top talent from MEta and Uber to strengthen its leadership team. Alongside this, its doubled its annual recurring reveneu to $100M in 2025 and is aiming to hit $200M by year-end. With its North platform for AI agents and a lean cost structure compared with larger rivals, Cohere is very much ready for more growth.
You'll be working on
- The integration team is responsible for developing and scaling machine learning algorithms and infrastructure for LLM post-training, with a focus on large-scale, distributed RL methods
- We strive for excellence in both engineering and science by meticulously designing experiments and design docs
- While tasks are assigned according to everyone’s expertise, there is a global team effort to write production code and support the team research efforts, depending on individual interests and organizational needs
- In particular, this role aims to enhance the global quality of the post-training codebase by implementing new tools to ease and support research, optimizing post-training algorithms, and scaling distributed RL to unprecedented levels
- Design and write high-performing and scalable software for training models
- Develop new tools to support and accelerate research and LLM training
- Coordinate with other engineering teams (Infrastructure, Efficiency, Serving) and the scientific teams (Agent, Multimodal, Multilingual, etc.) to create a strong and integrated post-training ecosystem
- Craft and implement techniques to improve performance and speed up our training cycles, both on SFT, offline preference, and the RL regime
- Research, implement, and experiment with ideas on our cluster and data infrastructure
- Collaborate, Collaborate, and Collaborate with other scientists, engineers, and teams!
Benefits
- Six weeks’ paid vacation
- Equity / stock options
- RRSP, 401(k), and Pension Scheme contributions
- Coverage for 100% of your insurance premiums across health, dental, vision, and travel
- Additional coverage for accessing mental health providers/services
- Six months of fully paid parental leave, including adoption and surrogacy
- Financial support for egg freezing and IVF in Canada and the UK
- A monthly fitness and wellness allowance
- Globally dispersed company that supports a remote work culture
- A $2,000 annual education benefit for professional development
- A weekly stipend for meals when working remotely and catered lunch when working from one of our global offices
- A monthly arts and culture allowance
- A monthly quality time allowance
We're looking for
- [Bonus] Experience in ML, LLM and RL academic research
- Extremely strong software engineering skills
- Value test-driven development methods, clean code, and strive to reduce technical debts at all levels
- [Bonus] Experience with distributed training infrastructures (Kubernetes) and associated frameworks (Ray)
- Proficiency in Python and related ML frameworks such as JAX, Pytorch and/or XLA/MLIR
- [Bonus] Hands-on experience with the post-training phase of model training, with a strong emphasis on scalability and performance
- Experience using and debugging large-scale distributed training strategies (memory/speed profiling)
- Have a deep passion for quality work
- Enjoy tuning and optimising large LLM models
- Comfortable diving into complex ML codebases to identify and resolve issues, ensuring the smooth operation of our systems
- Comfortable working with people with different levels of software engineering skills, from beginner to more advanced
- Thrive in a fast-paced, technically challenging environment, where you can contribute your innovative ideas and solutions