Machine Learning Engineer
Your future field of work
We are launching a set of new AI projects designed to deliver immediate, tangible value to our users. Just as importantly, we are building internal AI tooling to improve our software development processes and help our engineers build more effectively. This initiative involves projects spanning local LLMs, RAG systems, multi-agent workflows, and the semantic analysis of our engineering data.
What we don't have yet is a team that properly owns this. That's the job. You will be the senior engineer in the room from day one: setting the technical direction, deciding what we build versus what we buy, and helping us hire the people who come next.
Your role would include:
- Designing, building, and shipping machine learning solutions that run in embedded and real-time environments.
- Taking models the whole way: data, training, optimization, integration, deployment, and the unglamorous monitoring afterwards. Establishing the foundational MLOps infrastructure.
- Tailoring models for constrained targets (quantization, pruning) and making honest trade-offs between accuracy, memory, and latency.
- Working across diverse domains and business units (embedded, DTV, Android, IoT, automotive, CI/CD). Their constraints are your constraints, and most of your design decisions will be made in conversation with them.
- Turning advanced AI concepts into practical tools. You will build RAG systems and multi-agent workflows to solve real bottlenecks in daily engineering work.
- Defining what we need, interviewing candidates, and establishing the engineering standards, tooling, and review practices the team will work by.
- Providing structured engineering mentorship and helping us turn interns into colleagues.
- Communication with stakeholders across the company. Part of the job is explaining what AI can realistically do for a given product and being equally clear about what it can't.
Requirements:
- Experience shipping ML to production. You should be able to talk through something you built that real users depended on.
- Hands-on experience with embedded and/or real-time systems: memory and CPU budgets, debugging on target hardware.
- Advanced proficiency in Python.
- Practical experience with machine/deep learning frameworks, as well as the MLOps tooling required to support them.
- The ability to work without a fully defined roadmap. Early on, part of your job is deciding what the job is.
- University degree in computer technology or computer science.
- Very good knowledge of English language.
Preferred skills and knowledge:
- Experience applying AI to source code.
- A track record of sharing knowledge, whether through conference talks, internal tech presentations, or university lecturing.
Why you will love working here:
- Real, tangible impact at scale.
- Setting the direction.
- Always growing.
- Feel Good, Work Better.
- Your Time, Your Way.
- Global Adventures.
- Fun Beyond the Code.
