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- Tasks: Join a small team to enhance ML systems for real customer problems.
- 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 real impact by automating complex workflows with cutting-edge technology.
- Qualifications: Proficient in Python, experienced with LLM systems, and Docker.
- Other info: Small, senior team with a fast-paced, outcome-driven environment.
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 employer: Wave Talent
Contact Detail:
Wave Talent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the AI and machine learning space, especially those who work at companies you're interested in. A friendly chat can sometimes lead to job opportunities that aren't even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving LLMs and Python. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common ML engineering questions and scenarios. Think about how you’d tackle real-world problems, as this role is all about practical solutions and shipping improvements quickly.
✨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 Machine Learning Engineer
Some tips for your application 🫡
Show Your Passion for ML: When 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 this 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 high-accountability team and the role we’re hiring for.
Highlight Your Production Experience: Since this is a production-first role, make sure to emphasise your experience with Python in production environments. Talk about the projects you've shipped end-to-end and how you’ve tackled real customer problems. We love seeing ownership in action!
Tailor Your Application: Don’t just send a generic application! Tailor your CV and cover letter to reflect the specifics of the job description. Mention your familiarity with Docker and any relevant tools, and show us how your builder mindset aligns with our goals 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 and document processing. Be ready to discuss your hands-on experience with Python and any projects you've worked on that involved real customer problems.
✨Show Off Your Builder Mindset
This role is all about ownership and getting things done. Prepare examples of projects where you took an idea from concept to production. Highlight how you’ve iterated on your work based on feedback and results.
✨Get Comfortable with Docker
Since the role involves running containerised services, make sure you can talk confidently about your experience with Docker. If you have any specific examples of how you've used it in past projects, share those!
✨Communicate Clearly
In a small team, clear communication is key. Practice explaining complex concepts in simple terms, and be prepared to discuss how you collaborate with others. Show that you can operate effectively in a high-accountability environment.
