Member of Technical Staff, Senior/Staff MLE
Remote • San Francisco • FullTime
Posted 6mo ago
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Applied AI Engineer
About the job
Cohere is seeking a Member of Technical Staff, Applied ML to work directly with enterprise customers on challenging problems that push Large Language Models (LLMs) to their limits. In this role, you will rapidly understand customer domains, design custom LLM solutions, and deliver production-ready models that solve high-value, real-world problems. You will train and customize frontier models, leveraging Cohere's full stack including CPT, post-training, retrieval and agent integrations, model evaluations, and state-of-the-art modeling techniques. Your work will directly influence the capabilities of Cohere's foundation models, as techniques, datasets, evaluations, and insights developed for customers will shape the next generation of models. This role offers an opportunity to operate with early-startup level ownership within a frontier-model company, combining the breadth of an early-stage CTO with the infrastructure and scale of a deep-learning lab, and wearing multiple hats to define Applied ML at Cohere.
Responsibilities
- Lead the design and delivery of custom LLM solutions for enterprise customers.
- Translate ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methodologies.
- Build custom models using Cohere’s foundation model stack, CPT recipes, post-training pipelines (including RLVR), and data assets.
- Develop SOTA modeling techniques that directly enhance model performance for customer use-cases.
- Contribute improvements back to the foundation-model stack, including new capabilities, tuning strategies, and evaluation frameworks.
- Work closely with enterprise customers to identify high-value opportunities where LLMs can unlock transformative impact.
- Provide technical leadership across discovery, scoping, modeling, deployment, agent workflows, and post-deployment iteration.
- Establish evaluation frameworks and success metrics for custom modeling engagements.
- Mentor engineers across distributed teams.
- Drive clarity in ambiguous situations, build alignment, and raise engineering and modeling quality across the organization.
Requirements
- Strong ML fundamentals and the ability to frame complex, ambiguous problems as ML solutions.
- Fluency with Python and core ML/LLM frameworks.
- Experience working with large-scale datasets and distributed training or inference pipelines.
- Understanding of LLM architectures, tuning techniques (CPT, post-training), and evaluation methodologies.
- Demonstrated ability to meaningfully shape LLM performance.
- Experience engaging directly with customers or stakeholders to design and deliver ML-powered solutions.
- A track record of technical leadership at a team level.
- A broad view of the ML research landscape and a desire to push the state of the art.
- Bias toward action, high ownership, and comfort with ambiguity.
- Humility and strong collaboration instincts.
- A deep conviction that AI should meaningfully empower people and organizations.
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 office).
- A co-working benefit for those not near an office.
- A $500 home office stipend to set up your workspace properly.