Member of Technical Staff, Search

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!

Member of Technical Staff, Search

Why this role?

We are looking for talented individuals to help us develop state-of-the-art models for information retrieval as part of our Search team. This group is working on a range of tasks including training our embedding and reranker models. You'll have the opportunity to revolutionise people's search experience by contributing to building an intelligent, efficient and precise search system and you would have a lot of opportunity to try new things out, innovate, and productionize your ideas.

Your work will specifically focus on advancing semantic search techniques to improve accuracy and efficiency, involving working with a wide range of novel technologies and collaborating with other teams to integrate your work into our search infrastructure.

We're looking for someone who is passionate about search and has a strong background in information retrieval. Candidates should have experience working with a wide range of technologies and have worked collaboratively with other teams in the past.

As a Member of Technical Staff on this team, you will:

• Design, train and improve upon cutting-edge search models.

• Gather high-quality retrieval datasets and optimize data pipelines for model training and evaluation.

• Work closely with the model serving team to ensure that inference is fast and stable.

• Collaborate with product teams to develop solutions.

• Engage in research collaborations with our partner organizations and academic affiliations and publish your work in top-tier conferences and journals.

• Join us at a pivotal moment, shape what we build, have a strong ownership mindset, and wear multiple hats!

You may be a good fit if you have:

• Proficiency in Python and related ML frameworks such as PyTorch, Tensorflow, TF-Serving, JAX, and XLA/MLIR.

• Familiarity with training and using various information retrieval models.

• Experience leveraging Large Language Models as part of training data or evaluation pipelines.

• Strong communication and problem-solving skills.

Bonus points for:

• Experience building training and/or evaluation datasets for practical use cases.

• Proficiency in other programming languages, such as C++ or Golang.

• Experience using large-scale distributed training strategies with GPUs.

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, please submit an Accommodations Request Form, and we will work together to meet your needs.

We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.