openashbyhqradical
Member of Technical Staff, Pre-Training Data
Cohere
LocationToronto, San Francisco, New York, London, Paris, Montreal
WorkplaceRemote
EmploymentFullTime
Posted2025-06-25T11:34:46.521+00:00
Last observed2026-06-13 05:24:35.675610
Job idradical-cohere-2-735e1f36-781c-460d-afb7-2515d59858b7:ashbyhq:859e2e47-02fb-4afe-bb8a-e83bf4d8c265
Who are we? 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! Why this role? As a Machine Learning Engineer specializing in pretraining data, you will play a pivotal role in developing the data pipeline that underpins Cohere’s advanced language models. In this role, you will conduct data ablations to evaluate data quality and construct pre-training data mixtures to enhance model performance. 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, Pre-Training Data, you will: - 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 curriculum learning, data mixing and data attribution. - Familiarity with data processing frameworks such as Apache Spark, Apache Beam, Pandas, or similar tools. - Experience working with large-scale datasets, including web data, code data, and multilingual corpora. - Knowledge of data quality assessment techniques and experimentation with data mixtures. - 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). HOW AND WHERE WE WORK: - 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. 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, pl
This page is generated from the committed OpenOpps static snapshot. Use the source posting or apply link for the employer's current canonical posting state.