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For Companies At a Glance
- Tasks: Design and operate cutting-edge platforms for machine learning and data science.
- Company: Join a pioneering tech company at the forefront of computational innovation.
- Benefits: Enjoy a competitive salary, health perks, hybrid work, and growth opportunities.
- Why this job: Make a real impact in AI and scientific discovery while working with top-tier technology.
- Qualifications: Experience in platform engineering, cloud environments, and strong problem-solving skills.
- Other info: Collaborative team culture with exciting projects and career advancement potential.
The predicted salary is between 36000 - 60000 £ per year.
We are building a next-generation computational platform that powers large-scale machine learning, data science, and scientific discovery. Our teams work at the intersection of cloud infrastructure, high-performance computing, and data engineering, enabling researchers and ML practitioners to move faster—from experimentation to real-world impact.
This role sits at the heart of the platform: designing, scaling, and operating systems that support GPU-accelerated workloads, batch pipelines, and data-intensive applications.
Who This Role Is For (Choose Your Strength)
- AI Platform / ML Infrastructure Engineers
- Kubernetes-based compute platforms
- GPU scheduling, batch & distributed workloads
- Supporting ML training, inference, and experimentation at scale
- HPC / GPU Engineers
- Job schedulers, MPI, multi-node workloads
- Hybrid cloud and on-prem compute
- Performance, reliability, and cost optimisation
- Strong Data Engineers
- Large-scale data pipelines and data platforms
- Data reliability, orchestration, and observability
- Close collaboration with ML and research teams
What You'll Work On
- Designing and evolving Kubernetes-based compute platforms across hybrid and multi-cloud environments
- Building and operating GPU-enabled infrastructure for ML and scientific workloads
- Developing and maintaining core platform services, APIs, and internal tooling
- Improving CI/CD pipelines and Infrastructure-as-Code workflows
- Implementing monitoring, alerting, and reliability engineering practices
- Ensuring security, data protection, backup, and disaster recovery best practices
- Partnering closely with ML engineers, data scientists, and researchers to unblock compute and data challenges
What We're Looking For
- Strong experience in one or more of:
- Platform / infrastructure engineering
- ML infrastructure or MLOps
- HPC or GPU compute
- Data engineering at scale
Platform Engineer employer: Hlx Life Sciences
Contact Detail:
Hlx Life Sciences Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Platform Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can open doors that applications alone can't.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repo showcasing your projects, especially those related to Kubernetes, ML infrastructure, or data engineering. It’s a great way to demonstrate what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your problem-solving skills. Practice coding challenges and system design questions that relate to platform engineering. We want to see how you think on your feet!
✨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, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Platform Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights the skills and experiences that align with the Platform Engineer role. We want to see how your background in infrastructure engineering or data engineering can contribute to our next-gen computational platform.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're excited about this role and how your strengths fit into our team. We love seeing genuine enthusiasm for what we do at StudySmarter.
Showcase Relevant Projects: If you've worked on projects involving Kubernetes, GPU workloads, or data pipelines, make sure to mention them! We’re keen to see practical examples of your experience that demonstrate your problem-solving skills.
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 makes the process smoother for everyone!
How to prepare for a job interview at Hlx Life Sciences
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially Kubernetes, GPU scheduling, and CI/CD pipelines. Brush up on your Python skills too, as you might be asked to demonstrate your coding abilities or solve problems on the spot.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles, particularly those related to platform engineering or data pipelines. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight how you tackled complex issues.
✨Collaborate and Communicate
This role involves working closely with ML engineers and data scientists, so be ready to talk about your experience in collaborative environments. Share examples of how you’ve successfully partnered with other teams to overcome technical challenges and drive projects forward.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions that show your interest in the role and the company. Inquire about their current projects, the team dynamics, or how they measure success in this position. This not only demonstrates your enthusiasm but also helps you gauge if it’s the right fit for you.
