Engineering Manager - Machine Learning

Engineering Manager - Machine Learning

Full-Time 96052 - 127237 £ / year (est.) Home office (partial)
Menlo Ventures

At a Glance

  • Tasks: Lead a team to build and optimise machine learning infrastructure for drug discovery.
  • Company: Join Recursion, a pioneering company transforming lives through innovative technology.
  • Benefits: Competitive salary, annual bonus, equity compensation, and comprehensive benefits package.
  • Other info: Hybrid role based in London with excellent career growth opportunities.
  • Why this job: Make a real impact in healthcare by enabling cutting-edge AI/ML solutions.
  • Qualifications: Experience in MLOps, distributed systems, and a passion for mentoring others.

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.

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.

Engineering Manager - Machine Learning employer: Menlo Ventures

At Recursion, we are committed to fostering a collaborative and innovative work culture that empowers our employees to make a meaningful impact in the field of drug discovery. As an Engineering Manager in Machine Learning, you will not only lead a talented team but also have access to extensive growth opportunities, competitive compensation, and a comprehensive benefits package, all while working in the vibrant city of London. Join us in our mission to revolutionise healthcare through cutting-edge technology and a people-first approach.

Menlo Ventures

Contact Details:

Menlo Ventures Recruitment Team

StudySmarter Expert Advice🤫

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

Tip Number 1

Network like a pro! Reach out to people in the industry, especially those at Recursion. A friendly chat can open doors and give you insights that a job description just can't.

Tip Number 2

Show off your skills! If you’ve got a portfolio or any projects related to ML infrastructure, share them. It’s a great way to demonstrate your expertise and passion for the field.

Tip Number 3

Prepare for the interview by brushing up on your technical knowledge. Be ready to discuss MLOps, distributed systems, and how you can contribute to Recursion's mission. Confidence is key!

Tip Number 4

Don’t forget to 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 the team.

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

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

Some tips for your application 🫡

Show Your Passion for ML:When writing your application, let your enthusiasm for machine learning shine through! We want to see how excited you are about building scalable ML infrastructure and how it can change lives, including your own.

Be Specific About Your Experience:Make sure to highlight your hands-on experience in MLOps, distributed systems, and infrastructure. We love details, so share specific projects or challenges you've tackled that relate to the role!

Emphasise Teamwork and Mentorship:We value a people-first mindset, so don’t forget to mention your experience in mentoring and collaborating with others. Show us how you’ve supported your teammates in their growth and learning.

Apply Through Our Website:Finally, make sure to submit your application through our website. It’s the best way for us to keep track of your application and ensure it gets the attention it deserves!

How to prepare for a job interview at Menlo Ventures

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 previous roles. Highlight your people-first mindset and how you’ve supported colleagues in their growth, as this is crucial for the Engineering Manager position.

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.

Engage with Cross-Functional Teams

Demonstrate your ability to work collaboratively across different teams. Prepare to share experiences where you’ve successfully partnered with data scientists or researchers to translate their needs into scalable solutions.