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- Tasks: Join a small team to enhance ML systems for real customer challenges.
- Company: Early-stage UK AI company focused on innovative logistics solutions.
- Benefits: Competitive salary, equity options, and flexible remote work.
- Why this job: Make a tangible impact by shipping improvements weekly in a dynamic environment.
- Qualifications: Proficient in Python, with hands-on experience in LLM systems and Docker.
- Other info: Opportunity for rapid career growth and direct customer interaction.
The predicted salary is between 80000 - 90000 £ per year.
An early-stage UK AI company is building software that removes friction from complex logistics workflows by automating document processing and decision-making. Their product is already live with customers, and the focus now is on moving faster, shipping weekly, and compounding product quality through better ML systems.
You’ll join as one of the first ML engineers in a six-person team, working very close to the product and real customer problems. This is a production-first role, not research-led.
You’ll improve and extend LLM-powered document processing and agent workflows that are already in use. You’ll design and build evaluation datasets, run systematic experiments on prompts, models and architectures, and turn results into shipped improvements. You’ll help harden and scale a containerised ML pipeline, and keep a close eye on new model releases to identify practical gains the team can ship quickly. Ownership is end-to-end, from idea to production.
What you’ll bring:
- You’ve written a lot of Python that runs in production and you’re comfortable owning it.
- You’ve worked hands-on with LLM-based systems, including prompting, evaluation and iteration, and you care about measuring whether things are actually getting better.
- You’re confident with Docker and running containerised services in real environments.
- Experience with Go, workflow orchestration tools like Temporal, or building structured evaluation datasets is useful but not essential.
- More important is a builder mindset: you’ve shipped things end-to-end, you’re comfortable working independently, and you follow through on what you commit to.
- Clear communication and comfort operating in a high-bar, outcome-driven environment really matter here.
What’s on offer:
- Base salary of £80–90k depending on experience.
- Equity of around 0.1–0.2% in options, with scope for accelerated vesting.
- A genuinely small, senior team with a weekly shipping cadence and direct exposure to customers.
- UK-based today, with flexibility on remote working for the right person and openness to future New York expansion.
Apply to find out more!
Machine Learning Engineer in Sheffield employer: Wave Talent
Contact Detail:
Wave Talent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Sheffield
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those already working at companies you're interested in. A friendly chat can open doors and give you insider info that could help you stand out.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those involving LLMs and containerised services. This gives potential employers a taste of what you can do and how you think.
✨Tip Number 3
Prepare for interviews by practising common ML engineering questions and scenarios. Think about how you’d tackle real-world problems they might face, and be ready to discuss your past experiences with Python and Docker.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Machine Learning Engineer in Sheffield
Some tips for your application 🫡
Show Your Passion for ML: When you're writing your application, let us see your enthusiasm for machine learning! Share specific projects or experiences that highlight your hands-on work with LLM-based systems. We want to know what excites you about the field and how you've contributed to it.
Be Clear and Concise: We appreciate clarity in communication, so keep your application straightforward. Use bullet points where possible to outline your skills and experiences. This helps us quickly see how you fit into our team and the role we're looking to fill.
Highlight Your Ownership Experience: Since this role is all about end-to-end ownership, make sure to emphasise any projects where you've taken full responsibility. Describe how you’ve shipped things from idea to production, and don’t forget to mention any challenges you overcame along the way!
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 keen on joining our team at StudySmarter!
How to prepare for a job interview at Wave Talent
✨Know Your ML Stuff
Make sure you brush up on your machine learning fundamentals, especially around LLMs. Be ready to discuss your hands-on experience with document processing and how you've iterated on models in the past. They’ll want to see that you can talk about your work in a way that shows you understand both the technical and practical sides.
✨Show Off Your Python Skills
Since you'll be writing production-level Python, prepare to discuss specific projects where you've done this. Bring examples of code or projects that demonstrate your ability to own and improve systems. If you can, highlight any challenges you faced and how you overcame them.
✨Docker and Containerisation Know-How
Familiarity with Docker is key for this role. Be ready to explain how you've used Docker in previous projects, including any containerised services you've managed. If you have experience with orchestration tools like Temporal, don’t forget to mention that too!
✨Communicate Clearly and Confidently
This role requires clear communication, especially since you'll be working closely with a small team and real customers. Practice explaining complex concepts in simple terms. Show that you can articulate your ideas and listen actively, as collaboration will be crucial in this fast-paced environment.
