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- Tasks: Design and operate cutting-edge ML systems for healthcare professionals.
- Company: Foundation Health, a rapidly growing tech company in healthcare.
- Benefits: Stock options, life assurance, virtual GP access, and 25 days paid leave.
- Why this job: Make a real impact in healthcare with innovative ML solutions.
- Qualifications: Strong Python skills and hands-on experience with LLM frameworks.
- Other info: Join a dynamic team with hybrid working options and excellent career growth.
The predicted salary is between 36000 - 60000 £ per year.
Foundation Health powers major healthcare organisations - automating pharmacy communications, supporting patients on complex therapies and removing friction from critical healthcare workflows. Our platform is live, trusted and growing. After raising $20m at Series A, we’re accelerating product development and expanding our ML capabilities.
The Role: You’ll help define and build our production LLM systems, used directly by healthcare professionals and enterprise customers. This is not a research role - you’ll design, ship and operate ML systems that run reliably in a regulated environment. You’ll work closely with product and platform engineers, building architectural and model decisions whilst establishing best practices for evaluation, safety, observability and privacy. Over time, you’ll help shape and mentor a small ML team.
We’re looking for someone with strong software instincts, real production ML experience - who can bring good judgment around speed, quality and risk.
What You’ll Do:
- Design and operate customer-facing LLM systems in production
- Own the full ML lifecycle: model selection, evaluation, deployment, and iteration
- Build and improve RAG pipelines for real-world healthcare workflows
- Implement guardrails and safeguards, including PHI-sensitive protections
- Make informed decisions across open-source vs proprietary models, cost, latency, and quality
What We’re Looking For:
- Strong Python skills and hands-on experience with modern LLM frameworks
- Experience adapting or fine-tuning models using LoRA or equivalent techniques
- Proven experience evaluating models and using those insights to improve output
- Experience deploying models that power real products (B2B or consumer), not just internal tooling
- Good instincts for choosing the right model and switching as better options emerge, backed by clear measurements
- Deep familiarity with the open-source LLM ecosystem (e.g. Hugging Face)
- Practical experience with RAG systems
- Experience building or operating LLM safeguards, ideally in regulated or privacy-sensitive domains
What You’ll Get:
- Stock Options
- Group Life Assurance (via Legal & General), including: Death in Service Benefit (4x salary)
- Virtual GP access
- Care Concierge
- Employee Assistance Programme
- Auto-enrolment in the company pension scheme
- 25 days of paid annual leave plus bank holidays
- WeWork office in Manchester and London
- Hybrid flexible working arrangements
Interested? Hit apply, or drop a message to our Recruiter for a chat. Don’t worry if your CV isn’t up to date - we’ll cross that bridge later.
Machine Learning Engineering Lead in London employer: Foundation Health
Contact Detail:
Foundation Health Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineering Lead 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
Show off your skills! Create a portfolio showcasing your ML projects, especially those that relate to healthcare. This will give potential employers a taste of what you can do and how you think.
✨Tip Number 3
Prepare for interviews by brushing up on common ML scenarios and case studies. Be ready to discuss your past experiences and how they align with the role at Foundation Health. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining our team at Foundation Health.
We think you need these skills to ace Machine Learning Engineering Lead in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Machine Learning Engineering Lead role. Highlight your Python skills, production ML experience, and any relevant projects you've worked on. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about healthcare technology and how your background aligns with our goals. Be genuine and let us know what excites you about working with us at Foundation Health.
Showcase Your Projects: If you've worked on any LLM systems or relevant ML projects, make sure to mention them! We love seeing real-world applications of your skills. Include links to your GitHub or any demos if possible, so we can check out your work.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at Foundation Health
✨Know Your ML Stuff
Make sure you brush up on your machine learning knowledge, especially around LLM systems. Be ready to discuss your hands-on experience with Python and modern frameworks, as well as any projects where you've deployed models in real-world scenarios.
✨Showcase Your Problem-Solving Skills
Prepare to talk about specific challenges you've faced in previous roles, particularly around model selection and evaluation. Highlight how you made decisions based on speed, quality, and risk, and be ready to share examples of how you improved outputs using insights from your evaluations.
✨Understand the Healthcare Context
Since this role is in a regulated environment, it’s crucial to demonstrate your understanding of healthcare workflows and the importance of privacy-sensitive protections. Familiarise yourself with the specific challenges in healthcare ML applications and be prepared to discuss how you've navigated these in the past.
✨Be Ready to Discuss Team Dynamics
As you'll be mentoring a small ML team, think about your leadership style and how you’ve supported others in their growth. Prepare examples of how you've collaborated with product and platform engineers to build effective ML systems, and be ready to share your vision for fostering a strong team culture.
