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- Tasks: Build and scale advanced ML systems for real-world applications.
- Company: Exciting AI startup with a focus on innovation and growth.
- Benefits: Competitive salary, flexible work options, and opportunities for professional development.
- Other info: Fast-paced environment with great potential for career advancement.
- Why this job: Join a dynamic team and make a tangible impact in the AI field.
- Qualifications: 5+ years in ML systems, experience with production languages, and strong understanding of distributed systems.
The predicted salary is between 80000 - 100000 £ per year.
HUG are currently partnered with a well-funded, high-growth AI startup building advanced machine learning systems deployed in real-world production environments. They are hiring a Senior ML Systems Engineer to build and scale the infrastructure that enables cutting-edge ML models to move from research into production.
This is a highly technical IC engineering role sitting at the intersection of ML systems, infrastructure, and large-scale data. You will be responsible for building the platforms and systems that allow applied scientists to train, evaluate, and deploy models efficiently at scale. This role is not research-focused; it is about making ML systems work reliably in production. You’ll operate across the full lifecycle, from data ingestion through to inference and optimisation.
What You’ll Be Doing
- Build and scale data platforms for large, complex datasets
- Improve ML training infrastructure and data pipelines
- Develop tooling for dataset inspection, model evaluation, and experimentation
- Design systems for model versioning, lifecycle management, and deployment
- Optimise production inference pipelines and system performance across distributed/GPU environments
- Work closely with researchers to enable rapid experimentation and productionisation
What They’re Looking For
- 5+ years experience building production ML systems or ML infrastructure
- Experience deploying ML models at scale or building platforms/tools for ML teams
- Experience with a production language (e.g. C++, Java, Scala)
- Solid understanding of distributed systems
- Experience working with large-scale, high-volume datasets
- Experience in a startup or scale-up environment (ideally 50–300 people)
- Product-minded, able to balance technical depth with real-world impact
Nice to Have
- Experience with modern ML tooling (e.g. PyTorch, Ray, Triton, Spark, Iceberg)
- Background working with complex or non-standard data types
- Experience optimising performance across distributed or GPU systems
- Exposure to ML platform tooling for research teams
Senior ML Systems Engineer employer: HUG
Contact Detail:
HUG Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Systems Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and ML space on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML systems and infrastructure. We want to see how you’ve tackled real-world problems and made an impact.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and system design principles. We recommend practicing with mock interviews or coding challenges to get comfortable with the format.
✨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 proactive about their job search.
We think you need these skills to ace Senior ML Systems Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with production ML systems and infrastructure. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or tools you've worked with!
Craft a Compelling Cover Letter: Your cover letter is your chance to tell us why you’re the perfect fit for this role. Share your passion for building scalable ML systems and how your background makes you a great addition to our team. Keep it engaging and personal!
Showcase Your Technical Skills: Since this is a highly technical role, make sure to highlight your experience with languages like C++, Java, or Scala. We love seeing specific examples of how you've optimised systems or worked with large datasets in your previous roles.
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 don’t miss out on any important updates. Plus, we love seeing candidates who take that extra step!
How to prepare for a job interview at HUG
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technical skills listed in the job description. Brush up on your experience with production languages like C++, Java, or Scala, and be ready to discuss how you've built and scaled ML systems in the past.
✨Showcase Real-World Impact
Prepare examples that highlight how your work has made a tangible difference in previous roles. Focus on specific projects where you improved ML training infrastructure or optimised production inference pipelines, and be ready to explain the outcomes.
✨Understand the Startup Culture
Since this role is in a high-growth startup, demonstrate your adaptability and product-minded approach. Share experiences from your past that show how you’ve thrived in fast-paced environments and contributed to team success.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s ML systems and future projects. This shows your genuine interest in the role and helps you gauge if the company aligns with your career goals. Think about asking how they handle model versioning or what tools they use for dataset inspection.