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 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 the 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?
At Cohere, we strive to continually improve our large language models. Academic research and real-world experience has demonstrated that high quality, diverse datasets can contribute as much to the performance and capabilities of LLMs as the underlying model architecture and training regimen. We at Cohere believe data will play a central role in accelerating the advancement of our already world-class language models.
Data is therefore critical to our success. Our ability to acquire data that is accurate, relevant, and timely is key to our ability to improve the quality of our models. We strive to continuously improve our data acquisition processes and systems to ensure that we have the data we need to stay competitive and meet the needs of our customers. We run frequent experiments to learn more about the role of data for model quality, from data mixtures, to cleaning techniques, to quality control.
This role will be part of the Data Acquisition team, which broadly provides data for training models and is responsible for building and maintaining the infrastructure that acquires, cleans, and formats data for model training. We are looking for a technically skilled, resourceful problem-solver who is able to work in areas of ambiguity and find efficient and sometimes creative solutions. The main responsibility of this role is to improve our internal data acquisition infrastructure, which includes data crawlers, formatters, and integrations with data providers. This role would also work closely with different teams at Cohere to support their data acquisition needs, as well as engage in more experimental work to develop highly informative data signals.
Please Note: We have offices in Toronto, Palo Alto, and London but embrace being remote-first! There are no restrictions on where you can be located for this role.
As a Senior Software Engineer specializing in Data Acquisition, you will:
You may be a good fit if:
If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply! If you consider yourself a thoughtful worker, a lifelong learner, and a kind and playful team member, Cohere is the place for you.
We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants of all kinds and are committed to providing an equal opportunity process. Cohere provides accessibility accommodations during the recruitment process. Should you require any accommodation, please let us know and we will work with you to meet your needs.
Our 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 6 months for employees based in Canada, the US, and the UK
🎨 Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement
🏙 Remote-flexible, offices in Toronto, Palo Alto, San-Francisco and London and co-working stipend
✈️ 6 weeks of vacation
Note: This post is co-authored by both Cohere humans and Cohere technology.