Engineering Manager - Machine Learning in London

Engineering Manager - Machine Learning in London

London Full-Time 96052 - 127237 £ / year (est.) Home office (partial)
Recursion

At a Glance

  • Tasks: Lead a team to build and optimise machine learning infrastructure for drug discovery.
  • Company: Join Recursion, a pioneering TechBio company transforming lives through AI-driven drug discovery.
  • Benefits: Enjoy competitive salary, bonuses, equity, and comprehensive benefits in a hybrid work environment.
  • Other info: Be part of a dynamic team focused on innovation and continuous learning.
  • Why this job: Make a real impact in healthcare by enabling cutting-edge ML solutions for life-changing medicines.
  • Qualifications: Experience in MLOps, distributed systems, and a passion for mentoring and collaboration.

The predicted salary is between 96052 - 127237 £ per year.

Your work will change lives. Including your own.

The Impact You’ll Make

You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions.

In This Role You Will:

  • Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment.
  • Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams.
  • Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards.

The Team You’ll Join:

You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space.

The Experience You Will Need:

  • Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems.
  • Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems.
  • A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes.
  • Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture.
  • Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights & Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks.
  • Fluency in life sciences or drug discovery is a plus but not required to be considered.

Working Location & Compensation:

This is an office-based, hybrid position at our office located in London, England. Employees are expected to work in the office at least 50% of the time. At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is £96,052 to £127,237. You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package.

The Values We Hope You Share:

  • We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust.
  • We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.
  • We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.
  • We move with urgency because patients are waiting. Speed isn’t about rushing but about moving the needle every day.
  • We take ownership and accountability. Through ownership and accountability, we enable trust and autonomy—leaders take accountability for decisive action, and teams own outcomes together.
  • We are One Recursion. True cross-functional collaboration is about trust, clarity, humility, and impact. Through sharing, we can be greater than the sum of our individual capabilities.

Our values underpin the employee experience at Recursion. They are the character and personality of the company demonstrated through how we communicate, support one another, spend our time, make decisions, and celebrate collectively.

More About Recursion

Recursion (NASDAQ: RXRX) is a clinical-stage TechBio company decoding biology to radically improve lives. Recursion is advancing a portfolio of differentiated investigational medicines across its wholly owned and partnered pipeline in oncology, rare disease, neuroscience, immunology, and other therapeutic areas with significant unmet need. Enabling its mission is the Recursion OS, an AI-native, end-to-end drug discovery and development platform integrating biology, chemistry, and clinical development into a unified intelligence system. Powered by proprietary multimodal data, purpose-built AI models, and bilingual teams fluent in both science and AI, the Recursion OS is designed to translate complex science into medicines that matter — faster, better, and at scale — for patients who are waiting.

Recursion’s platform infrastructure is anchored in Salt Lake City, Utah and Milton Park, Oxfordshire, where its automated biology and chemistry laboratories generate proprietary data at industrial scale. Recursion also maintains offices in New York, Montréal, and London, three global hubs for talent and leadership at the intersection of AI and scientific innovation.

Learn more at www.recursion.com, or connect on X and LinkedIn. Recursion is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected under applicable federal, state, local, or provincial human rights legislation. Accommodations are available on request for candidates taking part in all aspects of the selection process.

Recruitment & Staffing Agencies: Recursion Pharmaceuticals and its affiliate companies do not accept resumes from any source other than candidates. The submission of resumes by recruitment or staffing agencies to Recursion or its employees is strictly prohibited unless contacted directly by Recursion’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Recursion, and Recursion will not owe any referral or other fees. Our team will communicate directly with candidates who are not represented by an agent or intermediary unless otherwise agreed to prior to interviewing for the job.

Engineering Manager - Machine Learning in London employer: Recursion

Recursion is an exceptional employer that fosters a collaborative and innovative work culture, where employees are empowered to make a meaningful impact on drug discovery through cutting-edge machine learning technologies. Located in London, the company offers competitive compensation, comprehensive benefits, and ample opportunities for professional growth and mentorship, ensuring that every team member can thrive while contributing to life-changing advancements in healthcare.

Recursion

Contact Details:

Recursion Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Engineering Manager - Machine Learning in London

Tip Number 1

Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.

Tip Number 2

Prepare for those interviews! Research the company, understand their ML infrastructure, and be ready to discuss how your experience aligns with their needs. Practise common interview questions and have your own questions ready to show your interest.

Tip Number 3

Showcase your projects! Whether it's a GitHub repo or a personal blog, having tangible examples of your work can set you apart. Make sure to highlight any relevant experience with MLOps, distributed systems, or machine learning models.

Tip Number 4

Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at Recursion.

We think you need these skills to ace Engineering Manager - Machine Learning in London

MLOps
Distributed Systems
Machine Learning Infrastructure
Model Deployment
GPU Optimization
Python
PyTorch

Some tips for your application 🫡

Tailor Your Application:Make sure to customise your CV and cover letter for the Engineering Manager role. Highlight your experience in MLOps, distributed systems, and any relevant projects that showcase your leadership skills. We want to see how you can make an impact at Recursion!

Show Your Passion for ML:Let your enthusiasm for machine learning shine through! Share specific examples of how you've engaged with ML technologies or contributed to projects that align with our mission. We love seeing candidates who are genuinely excited about the field.

Be Clear and Concise:When writing your application, keep it straightforward and to the point. Use clear language to describe your experiences and achievements. We appreciate a well-structured application that makes it easy for us to see your qualifications.

Apply Through Our Website:Don’t forget to submit your application through our official website! This ensures that your application gets to the right people and helps us keep track of all candidates. We’re looking forward to seeing what you bring to the table!

How to prepare for a job interview at Recursion

Know Your Tech Stack

Familiarise yourself with the technologies mentioned in the job description, like Python, PyTorch, and Kubernetes. Be ready to discuss how you've used these tools in past projects, especially in relation to MLOps and distributed systems.

Showcase Your Leadership Skills

Prepare examples of how you've mentored or coached team members in your previous roles. Highlight your people-first mindset and how you’ve fostered collaboration across teams to achieve common goals.

Understand the Business Impact

Be prepared to discuss how machine learning infrastructure can drive business outcomes. Think about how optimising GPU cluster utilisation or implementing MLOps standards can enhance efficiency and support rapid experimentation.

Ask Insightful Questions

Prepare thoughtful questions that show your interest in the role and the company’s mission. Inquire about the challenges the ML infrastructure team is currently facing or how they measure success in their projects.