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ML Systems Engineer - Edge AI
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Machine Learning Systems Engineer
Location: London/Hybrid - 1-2 days p/w in office
Position: Permanent
Salary: £100k-£110k + Benefits
We're partnering with an innovative technology company that's scaling its machine learning platform and is looking for a Machine Learning Systems Engineer to help take ML models from research into reliable, production-ready systems.
This is a hands-on engineering role focused on building the infrastructure, tooling and automation that enables machine learning models to be deployed, monitored and continuously improved across production environments. You'll work closely with Applied Scientists and Software Engineers to deliver scalable, resilient ML systems.
The Role
You'll play a key role in designing and operating the systems that underpin the ML lifecycle, including model training, deployment, serving and monitoring. As the platform continues to grow, you'll help improve reliability, scalability and automation while reducing manual operational effort.
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Key Responsibilities
- Design, build and maintain production machine learning infrastructure.
- Develop containerised Python services and APIs for model training and inference.
- Build and maintain automated deployment, evaluation and retraining pipelines.
- Manage model versioning, releases and production rollouts.
- Implement monitoring, logging and observability across ML systems.
- Collaborate with Applied Scientists to productionise research models.
- Drive improvements in automation, reliability and operational efficiency.
About You
You'll ideally have experience with:
- Strong Python software engineering skills.
- Building and operating machine learning systems in production.
- Model serving, inference pipelines or GPU-based workloads.
- Docker, Linux and CI/CD pipelines.
- API development using FastAPI or similar frameworks.
- Deploying software in on-premise, edge or customer-hosted environments.
- Monitoring, logging and production observability.
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Desirable Skills
- Kubernetes.
- MLflow, Weights & Biases or similar MLOps tooling.
- Airflow, Prefect, Kubeflow or other workflow orchestration tools.
- Time-series or telemetry data.
- Distributed training workloads.
- Industrial, IoT or edge computing environments.
Why Join?
- Work on technically challenging machine learning infrastructure.
- Collaborate with experienced engineers and applied scientists.
- Influence the design and evolution of a growing ML platform.
- Join a well-funded, high-growth technology business.
- Competitive salary, flexible working and excellent opportunities for career development.
If you're passionate about building robust production ML systems and want to solve complex engineering challenges at scale, we'd love to hear from you.
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