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- Tasks: Join a dynamic team to build and optimise ML tools for real-world medical applications.
- Company: Be part of a scaleup Medical AI firm revolutionising drug trial success rates.
- Benefits: Enjoy competitive salaries, stock options, and a meritocratic work environment.
- Why this job: Make a tangible impact in healthcare while collaborating with top talent in AI and tech.
- Qualifications: Strong background in Maths or Computer Science; experience with ML model deployment is essential.
- Other info: Work in a fast-paced, innovative setting with opportunities for growth and learning.
The predicted salary is between 36000 - 60000 £ per year.
We're looking for exceptional Machine Learning Engineers to join a scaleup Medical AI firm aiming to do wonders for drug trial success rates. With big investment and tie-ins secured, a large valuation already and top talent on board, this is one not to be missed!
You’ll build the ML tools and infrastructure that allow researchers, scientists and pharma clients to deploy and scale foundation models / large medicine models effectively — no SaaS platform, just highly tailored solutions with real-world impact.
What You’ll Do
- Work as part of a high performing team of academic, AI and technology specialists to integrate and scale ML models in hybrid environments (on-prem + AWS cloud).
- Own and improve ML infrastructure: model deployment, training pipelines, inference tooling.
- Diagnose and optimise performance of large-scale ML models.
- Build and maintain experiment tracking, monitoring, and observability systems.
- Collaborate with SWE and infra colleagues to build tooling for data access, cleaning, and delivery.
- Contribute to the internal “toolbox” enabling repeatable, scalable ML deployment across client teams.
- Work closely with researchers and strategy teams to bridge cutting-edge models with real-world use.
Successful candidates will likely have a subset of the following:
- Strong academic background in Mathematics, Computer Science, or related field.
- Experience deploying ML models at scale in real-world, high-performance environments.
- Fluency in PyTorch (or similar) environments, with experience in multi-node training and scale-up workflows.
- Deep understanding of ML Ops best practices: experiment tracking, data/version control, reproducibility.
- Ability to diagnose and tune model performance (both training and inference).
- Comfort navigating hybrid infrastructure: some workloads will be on-prem, others cloud (large GPU clusters).
- Familiarity with distributed systems and container orchestration (e.g., Kubernetes, Ray).
- Experience working client-facing or in cross-functional teams — ideally within pharma/life sciences.
- A “get stuck in” attitude — this is a team of doers, not just architects.
Bonus Points For
- Familiarity with NVIDIA tools (e.g., NSight, Triton Inference Server) is a major plus.
- Multi-modal model experience.
- 3D imaging experience.
- Interest in or exposure to pharma and computational biology use cases.
- Experience in fast-paced environments (e.g., startups, hedge funds, advanced R&D orgs).
The Team
You’ll join a growing, technical-first team with SWE and infra colleagues lined up, and work alongside researchers and strategists to support 4–5 high-value clients at a time. The firm offers competitive salaries plus stock options and bonus schemes, and aims to provide a meritocratic environment where performance is rewarded in a big way.
ML Engineer employer: Vertex Search
Contact Detail:
Vertex Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer
✨Tip Number 1
Familiarise yourself with the latest trends in machine learning, especially in the medical AI sector. This will not only help you understand the company's goals but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Network with professionals in the field of ML and pharma. Attend relevant meetups or webinars to connect with potential colleagues or mentors who can provide insights into the company culture and expectations.
✨Tip Number 3
Showcase your hands-on experience with ML tools and infrastructure by working on personal projects or contributing to open-source initiatives. This practical knowledge will be invaluable when discussing your capabilities with the hiring team.
✨Tip Number 4
Prepare to discuss specific examples of how you've optimised ML models in previous roles. Being able to articulate your problem-solving process and results will demonstrate your value to the team.
We think you need these skills to ace ML Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, particularly any work with model deployment and optimisation. Emphasise your familiarity with tools like PyTorch and any experience in hybrid environments.
Craft a Compelling Cover Letter: In your cover letter, express your passion for the role and the impact of AI in the medical field. Mention specific projects or experiences that align with the job description, showcasing your ability to contribute to their goals.
Showcase Technical Skills: Include a section in your application that details your technical skills, especially those related to ML Ops best practices, distributed systems, and any familiarity with NVIDIA tools. This will help demonstrate your fit for the role.
Highlight Team Collaboration: Since the role involves working closely with cross-functional teams, provide examples of past experiences where you successfully collaborated with others, particularly in high-performance environments like startups or R&D organisations.
How to prepare for a job interview at Vertex Search
✨Showcase Your Technical Skills
Be prepared to discuss your experience with ML models, particularly in PyTorch or similar environments. Highlight specific projects where you've deployed models at scale and the impact they had.
✨Demonstrate Problem-Solving Abilities
Expect technical questions that assess your ability to diagnose and optimise model performance. Prepare examples of challenges you've faced in previous roles and how you overcame them.
✨Understand the Company’s Mission
Research the firm’s focus on improving drug trial success rates. Be ready to discuss how your skills can contribute to their goals and the real-world impact of your work.
✨Emphasise Team Collaboration
Since the role involves working closely with cross-functional teams, share experiences that highlight your ability to collaborate effectively. Discuss how you’ve worked with researchers or other specialists in past projects.
