Machine Learning Manager - Applied ML (UK/EU)

Cohere
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

AI Engineer / Machine Learning Engineer

AI Engineer / Machine Learning Engineer

Lead Data Scientist

Applied Data Scientist

Data Scientist (NLP & LLM Specialist)

Senior Data Scientist

Overview

We are looking for a Machine Learning Manager to help lead our Applied ML team in building and delivering cutting-edge AI solutions tailored for enterprise customers. This is a high-impact leadership role that combines strategic direction, technical oversight, and customer collaboration. You'll manage a world-class team of ML engineers focused on building scalable, production-grade systems-working across modalities and domains such as reasoning, code, RAG, tools, and agents.

The ideal candidate brings deep expertise in AI/ML, a strong track record of building and mentoring high-performing teams, and the ability to turn complex technical capabilities into real business outcomes.

Key Responsibilities

Strategic Leadership

  • Define and drive the long-term vision for the Applied ML team in alignment with Cohere's product and business goals.
  • Shape the roadmap for custom model development, fine-tuning, and advanced implementations that address nuanced enterprise challenges.
  • Collaborate closely with executive leadership to prioritize high-impact initiatives and strategic customer engagements.

Team Management

  • Lead and grow a high-performing team of ML engineers through hiring, coaching, and mentorship.
  • Foster a culture of ownership, innovation, and continuous learning.
  • Establish and evolve team processes to maximize productivity and execution speed.

Product & Technical Innovation

  • Partner with Product to define and deliver novel, scalable ML solutions that differentiate Cohere in the market.
  • Guide the development of reusable frameworks and abstractions that streamline deployment across customer use cases.
  • Oversee performance optimization and evaluation of models in real-world enterprise environments.

Customer Engagement

  • Act as a trusted technical advisor to strategic customers-translating needs into actionable plans.
  • Lead delivery efforts from prototyping through to production deployment on customer infrastructure.
Ideal Candidate Profile

Experience

  • Bachelor's degree in Computer Science, Machine Learning, or a related field (Master's or PhD preferred).
  • 8+ years in AI/ML, including several years in technical leadership roles.
  • Proven success leading large ML teams and delivering complex AI solutions at scale.
  • Experience with enterprise deployments, including custom model development and fine-tuning.

Technical Expertise

  • Deep understanding of LLMs, their training, deployment, and real-world constraints.
  • Hands-on experience with RAG pipelines, agentic systems, and multi-modal applications.
  • Proficiency in ML frameworks such as PyTorch or TensorFlow.
  • Familiarity with modern cloud platforms (AWS, GCP, Azure) and ML infrastructure best practices.

Strategic & Business Acumen

  • Strong ability to translate business requirements into scalable ML solutions.
  • Track record of product thinking and technical decision-making aligned with customer needs.

Communication & Leadership

  • Exceptional communicator, capable of aligning technical execution with business goals.
  • Experienced mentor who inspires excellence, collaboration, and growth in technical teams.

If some of the above doesn't line up perfectly with your experience, we still encourage you to apply! If you want to work really hard on a glorious mission with teammates that want the same thing, Cohere is the place for you.

We value and celebrate diversity and 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.

Benefits

Full-Time Employees at Cohere enjoy these 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, New York, San Francisco, London and Paris, as well as a co-working stipend
  • 6 weeks of vacation

Note: This post is co-authored by both Cohere humans and Cohere technology.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.