Why This Job is Featured on The SaaS Jobs
This Member of Technical Staff role sits at a distinctly SaaS-relevant intersection: building the data machinery that makes frontier AI models usable by developers and enterprises. Synthetic data pipelines are becoming core product infrastructure for AI platforms, because they shape model quality, cost efficiency, and reliability at scale. The remit described here connects directly to how AI capabilities translate into features like semantic search, RAG, and agents that are increasingly delivered as APIs and managed services.
From a SaaS career perspective, the work builds durable leverage in operating production-grade ML systems rather than isolated experiments. Owning end-to-end synthetic data generation, ablations, and evaluation creates fluency in iteration loops that matter in SaaS: measuring impact, improving throughput, and optimizing expensive compute. Experience with large datasets, GPU inference pipelines, and cross-functional collaboration tends to transfer well across AI-first SaaS companies where platform performance becomes a competitive constraint.
This role best fits engineers who prefer deep technical ownership and enjoy bridging research ideas into dependable systems. It also suits professionals who want their impact to be legible in metrics and model behavior, and who are comfortable working across data engineering, ML tooling, and evaluation practices in a remote-friendly environment spanning time zones.
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 Machine Learning Engineer specializing in synthetic data, you will play a pivotal role in developing the synthetic data pipeline that is crucial to Cohere’s advanced language models. Your responsibilities will encompass the end-to-end management of synthetic data, including maintaining and optimizing the synthetic data pipeline, data analysis and generation, as well as conducting data ablations and model evaluation to gauge data quality. You will work with diverse web data and code data and transform them using generative models to improve token efficiency and model quality. By combining research and engineering, you will bridge the gap between raw data and cutting-edge AI models, directly contributing to improvements in critical training metrics like throughput and accelerator utilization.
Your work will be essential to Cohere’s mission of delivering efficient and reliable language understanding and generation capabilities, driving innovation in natural language processing. If you are passionate about transforming data into the foundation of AI systems, this role offers a unique opportunity to make a meaningful impact.
Please Note: We have offices in London, Paris, Toronto, San Francisco and New York but also embrace being remote-friendly! There are no restrictions on where you can be located for this role between EST and EU
As a Member of Technical Staff, Synthetic Data, you will:
Design and build scalable inference pipelines that run on large GPU clusters.
Conduct data ablations to assess data quality and experiment with data mixtures to enhance model performance.
Research and implement innovative synthetic data curation methods, leveraging Cohere’s infrastructure to drive advancements in natural language processing.
Collaborate with cross-functional teams, including researchers and engineers, to ensure data pipelines meet the demands of cutting-edge language models.
You may be a good fit if you have:
Strong software engineering skills, with proficiency in Python and experience building data pipelines.
Familiarity with data processing frameworks such as Apache Spark, Apache Beam, Pandas, or similar tools.
Experience working with LLMs through work projects, open-source contributions or personal experimentation.
Familiarity with LLM inference frameworks such as vLLM and TensorRT.
Experience working with large-scale datasets, including web data, code data, and multilingual corpora.
A passion for bridging research and engineering to solve complex data-related challenges in AI model training.
Bonus: paper 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!)
