Member of Technical Staff - Post-training (Data Infrastructure)

Cohere

United States +1 more

On-siteCareer-pivot friendly

Who this role is best for

Aimed at mid-to-senior level data engineers with ML experience who build and maintain data pipelines for LLM post-training, working in a remote-friendly but office-aligned environment with a focus on collaboration with researchers and engineers.

Best fit for

Candidates with experience in large-scale data pipeline design for LLM post-training and strong communication skills to collaborate with researchers.
— “Experience designing and building large-scale data pipelines for machine learning, ideally for LLM post-training.”
Individuals who have a track record in distributed data processing and model inference at scale, ensuring efficient system performance.
— “Track record of running distributed data processing and model inference at scale.”
Candidates who are passionate about maintaining dataset quality, versioning, and reproducibility in high-stakes AI development.
— “Own dataset quality, versioning, and reproducibility.”

Things to consider

The role requires a candidate to be comfortable working across multiple global offices and time zones.
— “We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul.”
The position demands a high level of coordination with researchers and annotation teams, which may require flexibility in workflow.
— “Work closely with researchers and annotation teams so they can iterate on data as quickly as they iterate on models.”

How to stand out

Highlight your experience in scaling data pipelines and model inference for enterprise AI systems.
— “Scale model inference for data generation and model-based grading.”
Showcase your ability to design reusable data components and ensure reproducibility in data processing.
— “Turn research data recipes into production-grade, reusable components.”
Emphasize your background in post-training methods like SFT, RLHF, and DPO to align with Cohere’s technical focus.
— “Good understanding of post-training methods (e.g., SFT, RLHF, DPO) and how data shapes model behaviour.”
Demonstrate your ability to work in a global, remote-friendly environment with cross-team collaboration.
— “Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul.”

Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

Built and maintained efficient data pipelines for post-training data
Ensured dataset quality and reproducibility
Collaborated effectively with cross-functional teams

Typical background

Strong software engineering backgroundExperience in data engineering

Skills & requirements

Required

Data Pipeline DesignData Quality ManagementVersion ControlModel InferenceData Formats And SchemasCollaboration With Researchers And Engineers

Preferred

Large Language Models ExperienceSynthetic Data Generation

Stack & domain

Data PipelinesMachine LearningModel InferenceData FormatsCommunicationCollaborationData InfrastructureAI

About the role

Original posting from Cohere via Ashby

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 headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!
Role Overview
As a Member of Technical Staff in Post-Training Data Infrastructure, you will build and own the pipelines that produce the post-training data for our large language models (LLMs), from SFT and RL data to synthetic and human-annotated data. You will work closely with researchers and annotation teams so they can iterate on data as quickly as they iterate on models.
Key Responsibilities
Build and maintain data pipelines for SFT, preference, and RL post-training data.
Own dataset quality, versioning, and reproducibility.
Turn research data recipes into production-grade, reusable components.
Scale model inference for data generation and model-based grading.
Design data formats and schemas that keep post-training datasets consistent and efficient to train on.
Collaborate with researchers and engineers to make sure data pipelines keep pace with the needs of cutting-edge language models.
Qualifications
Strong software engineering skills.
Experience designing and building large-scale data pipelines for machine learning, ideally for LLM post-training.
Track record of running distributed data processing and model inference at scale.
Good understanding of post-training methods (e.g., SFT, RLHF, DPO) and how data shapes model behaviour.
Excellent communication skills.
This is neither an exhaustive nor necessary set of attributes. Even if none of these apply to you, but you believe you will contribute to Cohere, please reach out. We value diverse backgrounds and perspectives at Cohere
FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS:
A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.
Full health and dental benefits, including a separate budget for mental health.
RRSP matching, 401K, Pension Scheme.
100% Parental Leave top-up for up to 6 months, for either parent.
Annual enrichment benefits:
Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.
Education & learning stipend for conferences, courses, and coaching.
6 weeks of paid vacation (30 working days!)
Budget for traveling to other offices if you are remote, plus an annual company offsite.
HOW AND WHERE WE WORK:
Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening 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.
Everyone receives a $500 home office stipend to set up your workspace properly.
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 https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, 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.
Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Source: Cohere careers (Ashby)