Senior ML Engineer
Confirmed on the employer's own hiring board on Oct 19, 2026. First seen by Alion on Oct 1, 2026. Teamup Recruitment scores A on the Alion truth index.
Our client is building AI-powered applications that help people manage everyday activities with less effort. The aim is to make intelligent assistance accessible without requiring users to learn complex prompting techniques.
The product is designed to understand ongoing context, support extended workflows, and complete practical tasks reliably. A key engineering priority is reducing incorrect outputs and ensuring that AI capabilities translate into dependable results.
The broader ambition is to simplify everyday organisation, giving people more time for meaningful activities.
Role
As a Senior Member of Technical Staff, Machine Learning, you will take responsibility for important ML components throughout their production lifecycle. You will turn open-ended challenges into clear technical approaches and deliver solutions that perform consistently as usage grows.
This is a hands-on position for an engineer who combines deep ML expertise with strong execution and independent technical judgment.
Focus
Develop the ML capabilities behind an AI product that anticipates user needs and handles tasks over extended periods.
Lead delivery across the full lifecycle, from preparing datasets and training models to validation, serving, and ongoing refinement.
Translate promising research approaches into dependable production implementations.
Investigate model behaviour and operational failures using evidence from live systems.
Run short development cycles, assess results, and use those findings to guide improvements.
Partner with researchers, product specialists, and software engineers to deliver useful capabilities.
Support other ML engineers through thoughtful code reviews, mentoring, and practical technical guidance.
Make engineering decisions within production requirements for response times, operating costs, reliability, and safety.
Tech Stack
Python
Deep learning frameworks, including PyTorch and JAX
GPU infrastructure for model training and inference
Ideal Experience
A track record of delivering ML solutions that have been deployed and used in real products.
A practical understanding of modern model capabilities, limitations, and failure patterns in production.
Strong software engineering skills, with an emphasis on maintainable code and coherent system design.
The ability to take responsibility for complex work, operate independently, and carry solutions through to delivery.
An adaptable approach to learning, clear communication, and a willingness to improve through testing and feedback.
Outcomes
Production models consistently achieve the required levels of quality, responsiveness, stability, and resource efficiency.
Operational problems are detected early, investigated effectively, and resolved with limited impact on users.
Data processing, training, and inference workflows remain dependable and manageable as the product evolves.
Improvements are supported by measurable results from production usage and user feedback.
Peer support and technical guidance strengthen the quality of ML engineering across the team.
ML capabilities work smoothly within the wider product and support its business objectives.
How We Work
The team operates with a small group of highly capable specialists who stay closely involved in implementation. Decisions are made collaboratively, with fast execution and a strong emphasis on both quality and learning from released work.
Engineers are expected to bring clarity to uncertain problems, make sound decisions, and move work forward independently. The shared ambition is to create an intuitive product that makes AI useful in everyday life.
Interview Process
Candidates whose experience aligns with the role will be invited to a focused process consisting of three interviews, with a fourth meeting added where needed.
Applications are reviewed by members of the technical team. Interviews may take place remotely, in person, or through a combination of both formats.
The process is designed to be clear and efficient, with timely feedback and a prompt hiring decision. Successful candidates will join a team working to turn advanced AI into practical capabilities for a broad audience.