Why This Job is Featured on The SaaS Jobs
This role sits at a core SaaS inflection point where AI capabilities become a product surface for developers and enterprises. Building pretraining data pipelines is not internal analytics work; it is foundational infrastructure that directly influences model performance, reliability, and the feasibility of shipping AI features such as semantic search, RAG, and agents. The remit signals a company operating at the frontier-model layer, where data quality and throughput are first-order product constraints.
For a SaaS career, the long-term value is in learning how data engineering decisions translate into measurable changes in product capability and operating efficiency. The work spans ingestion, curation, and optimization across heterogeneous sources, plus experimentation through ablations and mixtures, which builds an applied understanding of iteration loops common in AI-enabled SaaS. Experience here transfers to other platform teams where scale, reproducibility, and cost-aware performance matter.
The role is best suited to engineers who want ownership over end-to-end pipelines and are comfortable working at the boundary of research and production systems. It fits professionals motivated by ambiguous, metrics-driven engineering problems and collaboration across disciplines, especially those aiming to deepen expertise in large-scale data processing within AI product organizations.
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 Data Engineer specializing in pretraining data, you will play a pivotal role in developing the data pipeline that underpins Cohere’s advanced language models. Your responsibilities will encompass the end-to-end management of training data, including ingestion, cleaning, filtering, and optimization, as well as data modeling to ensure datasets are structured and formatted for optimal model performance. You will work with diverse data sources, such as web data, code data, and multilingual corpora, to ensure their quality, diversity, and reliability. 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, Data Engineering, you will:
Design and build scalable data pipelines to ingest, parse, filter, and optimize diverse web datasets.
Conduct data ablations to assess data quality and experiment with data mixtures to enhance model performance.
Develop robust data modeling techniques to ensure datasets are structured and formatted for optimal training efficiency.
Research and implement innovative 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 large-scale web datasets like CommonCrawl.
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!)
