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 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 in London employer: Menlo Ventures
At Recursion, we are committed to fostering a collaborative and innovative work environment where your contributions directly impact the future of drug discovery. Our London office offers a vibrant culture that prioritises employee growth through mentorship and cross-functional collaboration, alongside competitive compensation and comprehensive benefits. Join us to be part of a mission-driven team that values your expertise in machine learning while providing opportunities for continuous learning and professional development.
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 people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨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 think about how you can showcase your leadership skills.
✨Tip Number 3
Show off your projects! If you've worked on any relevant ML projects, make sure to highlight them during interviews. Bring along a portfolio or a GitHub link to demonstrate your hands-on experience and technical prowess.
✨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 and making an impact in the world of drug discovery.
We think you need these skills to ace Engineering Manager - Machine Learning in London
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 how you can bring that to our team.
Apply Through Our Website:Finally, make sure to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. We can’t wait to hear from you!
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 role.
✨Understand the Business Impact
Be prepared to discuss how machine learning infrastructure can drive business outcomes. Think about how your work can enable rapid experimentation and reliable model deployment, and be ready to share your thoughts on optimising GPU cluster utilisation.
✨Ask Insightful Questions
Prepare thoughtful questions that show your interest in the company’s ML culture and infrastructure challenges. Inquire about their current projects or future goals, which will demonstrate your enthusiasm and strategic thinking.