Why This Job is Featured on The SaaS Jobs
Frontier-model providers increasingly sit at the core of the SaaS ecosystem, supplying developer and enterprise customers with foundational capabilities that power search, RAG, and agentic workflows. This role stands out because it targets agent safety, a domain that directly shapes whether AI features can be shipped responsibly inside production SaaS products where models take actions and interact with external tools.
For a SaaS-oriented career, the work builds durable expertise in evaluation, data generation, and post-training methods that translate into practical product risk management. Experience collaborating across engineering, product, and policy functions maps closely to how SaaS companies operationalise reliability and governance, especially as AI systems move from experimentation into customer-facing workflows. The emphasis on measurement, robustness, and secure behaviour also aligns with the growing expectation that AI teams can define quality bars, not just model performance.
The position is best suited to engineers or applied researchers who prefer ambiguous problem spaces and are comfortable moving between experimentation and production-grade implementation. It will appeal to professionals who like working with messy real-world data, can communicate results clearly, and want their technical decisions to influence how AI capabilities are delivered to SaaS customers at scale.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Who are we?
Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI.
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. We like to work hard and move fast to do what’s best for our customers.
Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products.
Join us on our mission and shape the future!
Why this role?
As a Member of Technical Staff in the Safety for Agents team, you will make a meaningful impact on the development of better, fairer, more trustworthy, and more secure Large Language Models (LLMs). Your primary focus will be on data generation, post-training algorithms, and evaluation methods to ensure Safety in the next generation of models that can access external resources and take actions in the world. You will work closely with other cross-functional machine learning teams and data annotation teams, and will also collaborate with product and policy teams.
This role combines expertise in machine learning, ethical and responsible AI, experimental design, and data generation and management.
It will require curiosity to tackle totally new scientific problems, engineering skills to implement the pieces we need to test solutions to these, and a desire to dive into messy data and results. You will be on a small team with a lot of autonomy and decision-making power, responsible for making the next generation of LLMs better for society as a whole.
Please Note: The existing team work in offices in London, Edinburgh, Paris, Toronto, and New York, but we also embrace being remote-friendly! For this role you need to have ~50% working day overlap with UK/EU timezone (e.g. US East is fine) but there are otherwise no restrictions on where you can be located for this role.
You may be a good fit if you have:
Strong statistical skills and experience evaluating scientific experiments related to data collection and model performance.
Extremely strong software engineering skills.
Strong expertise in designing and conducting data collection tasks, including working with human annotators.
Experience analyzing datasets with respect to their quality, biases, and suitability for training ML models.
Hands-on experience training large language models (LLMs) on distributed training infrastructures.
Familiarity with evaluating and improving the generalizability and robustness of ML systems.
Proficiency in programming languages such as Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX).
Excellent communication skills to collaborate effectively with cross-functional teams and present findings.
One or more papers at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP).
If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply!
We value and celebrate diversity and 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.
Full-Time Employees at Cohere enjoy these Perks:
🤝 An open and inclusive culture and work environment
🧑💻 Work closely with a team on the cutting edge of AI research
🍽 Weekly lunch stipend, in-office lunches & snacks
🦷 Full health and dental benefits, including a separate budget to take care of your mental health
🐣 100% Parental Leave top-up for up to 6 months
🎨 Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement
🏙 Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend
✈️ 6 weeks of vacation (30 working days!)
