Lead ML Engineer: Edge AI & On‑Site (Bristol)

Rowden; West of England; Hybrid; GBP 90,000 - 120,000; Full time

Job summary

Rowden, Bristol-based, is building the UK's next generation engineering powerhouse focusing on sensing, AI, and secure edge deployments. The Lead Machine Learning Engineer will head a rapidly growing ML team, balancing hands-on engineering (~70%) with team leadership (~30%), reporting to the Head of ML.

The role requires real ML expertise, mentoring, and cross‑disciplinary collaboration across deep learning, data engineering, and LLM/agentic systems.

Qualifications

• MSc or equivalent experience required.
• Hands-on ML engineering and leadership capability.
• Experience leading ML teams and setting engineering standards.
• Proven ability to mentor engineers and manage performance.

Responsibilities

• Lead and develop your team: line manage engineers, set objectives, support progression, be accountable for team performance.
• Set what good looks like: define standards for ML engineering, from code quality to evaluation and docs.
• Build how we work: develop processes and systems for planning, review, knowledge sharing and shipping.
• Grow the craft: mentor, review, and introduce new practices as the team grows.
• Resource and hire: place the right people on projects and lead ML hiring.
• Stay hands-on: spend ~70% of time on project work as an ML engineer while leading the team.

Skills

Python

ML engineering

Leadership

People management

Edge deployment

Communication

Mentoring

Education

MSc or equivalent experience

Tools

Databricks

AWS

GCP

GitHub

Docker/Kubernetes

MLflow

Jira

Job description

Rowden, Bristol-based, is building the UK's next generation engineering powerhouse focusing on sensing, AI, and secure edge deployments. The Lead Machine Learning Engineer will head a rapidly growing ML team, balancing hands-on engineering (~70%) with team leadership (~30%), reporting to the Head of ML.

The role requires real ML expertise, mentoring, and cross‑disciplinary collaboration across deep learning, data engineering, and LLM/agentic systems.