Principal Machine Learning Engineer – Production Systems

🔒 Confidential Employer
Posted 7 May 2026
LOCATION
Bristol
TYPE
Full-time
LEVEL
Mid-Senior level
CATEGORY
Software Engineering
This employer holds a UK Home Office sponsor license — sponsorship for this specific role is at the employer’s discretion

SKILLS

TensorFlow ONNX Runtime Python gRPC MLOps Docker Kubernetes CUDA

FULL DESCRIPTION

Principal Machine Learning Engineer – Production Systems

[Employer hidden — sign up to reveal] is seeking a highly experienced ML Systems Architect to design and implement a scalable, production-grade architecture for our machine learning solver. This role bridges research prototypes and commercial deployment, ensuring reliability, maintainability, and performance in a mixed technology stack.

Responsibilities

  • Architect the ML Solver Platform: Define modular architecture for data preprocessing, model execution, and post-processing. Establish clear API contracts between Python/TensorFlow and C# services.
  • Productionize ML Workflows: Convert research code into robust, testable, and observable services. Implement CI/CD pipelines, automated testing, and reproducibility standards.
  • Integration & Interoperability: Design REST/gRPC endpoints for cross-language communication. Ensure compatibility with C#/.NET services.
  • Performance & Scalability: Optimize GPU/CPU utilization, batching strategies, and memory management. Plan for multi-model and multi-tenant scenarios.
  • MLOps & Lifecycle Management: Implement model versioning, artifact registries, and deployment workflows. Set up monitoring, logging, and alerting for solver performance.
  • Security & Compliance: Apply best practices for secrets management, dependency scanning, and secure artifact storage.

Required Skills & Experience

  • Expert in TensorFlow (TF2/Keras), experience with ONNX Runtime for inference.
  • Advanced Python for ML; strong understanding of packaging, type checking, and performance profiling.
  • Proven experience designing scalable ML systems for production.
  • Proficiency in gRPC/Protobuf and REST for cross-language integration.
  • CI/CD pipelines, containerization (Docker/Kubernetes), model registries, reproducibility.
  • GPU acceleration (CUDA/cuDNN), mixed precision, XLA, profiling.
  • Metrics, tracing, structured logging, dashboards.
  • SBOM, image signing, role-based access, vulnerability scanning.

Preferred Qualifications

  • Experience with ONNX Runtime Training, PyTorch, or hybrid ML architectures.
  • Familiarity with distributed training strategies and multi-GPU setups.
  • Knowledge of feature stores and data validation frameworks.
  • Exposure to regulated environments and compliance frameworks.

Tools & Technologies

  • ML: TensorFlow, ONNX Runtime, tf2onnx.
  • APIs: FastAPI, gRPC.
  • DevOps: GitLab CI/GitHub Actions, Docker, Kubernetes.
  • Monitoring: Prometheus, Grafana, OpenTelemetry.
  • Security: HashiCorp Vault, Sigstore.

Why Join [Employer hidden — sign up to reveal]?

  • Work on cutting-edge ML solutions integrated into commercial engineering software.
  • Define architecture that scales across global deployments.
  • Collaborate with a team of experts in ML, software engineering, and UI development.

To apply: Send your resume and a brief cover letter to [Employer hidden — sign up to reveal]

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