Member of Technical Staff, Agentic Environments

Cohere

1d•Hybrid

About The Position

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! At Cohere, we're pushing the boundaries of what these technologies can achieve for production environments, and we're seeking a senior engineer who understands the practical challenges of making AI systems work at scale. This role demands deep technical expertise in production environments and the ability to bridge research concepts with real-world implementations. This position is hands-on and engineering-driven. You'll focus on production systems, experiment with frontier models, and collaborate with talented engineers to build scalable, production ready solutions. We strive for excellence in both engineering and science by meticulously designing experiments and design docs. We blend engineering and research seamlessly. Everyone contributes to both, depending on organizational needs. We provide access to world class compute resources, data, and talent to ensure you can do your best work.

Requirements

Extremely strong software engineering skills.
Value test-driven development methods, clean code, and strive to reduce technical debts at all levels.
Proficiency in Python and related ML frameworks such as JAX, Pytorch and/or XLA/MLIR.
Experience using and debugging large-scale distributed training strategies (memory/speed profiling).
Experience with distributed training infrastructures (Kubernetes) and associated frameworks (Ray).
Experience in ML and LLM academic research.

Nice To Haves

Have a deep passion for quality work.
Enjoy tuning and optimising large LLM models.
Comfortable working with people with different levels of software engineering skills, from beginner to more advanced.
Comfortable diving into complex ML codebases to identify and resolve issues, ensuring the smooth operation of our systems.
Thrive in a fast-paced, technically challenging environment, where you can contribute your innovative ideas and solutions.

Responsibilities

Design and write high-performing and scalable software for training models.
Develop new tools to support and accelerate research and LLM training.
Coordinate with other engineering teams (Infrastructure, Efficiency, Serving) and the scientific teams (Agent, Multimodal, Multilingual, etc.).
Research, implement, and experiment with ideas on our cluster and data infrastructure.
Collaborate, Collaborate, and Collaborate with other scientists, engineers, and teams!

Benefits

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.
A daily lunch program, plenty of snacks, and regular community and social events (for those in the office).
A co-working benefit so you can work alongside others in your city (for those not near an office).
$500 home office stipend to set up your workspace properly.