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This listing was closed on Oct 16, 2026.
Browse Open JobsStaff Machine Learning Engineer - Ops
Summary by AIEngineer.careers
Staff Machine Learning Engineer - Ops is a MLOps Engineer role (full-time). with Wayve. in GREATER LONDON, GB. Imported listing (source: aihiringboard.com). Apply on the employer's site (aihiringboard.com).
Imported listing (source: aihiringboard.com) · Apply on aihiringboard.com
GREATER LONDON · GB
—
Full-time
STAFF
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from aihiringboard.com
Wayve is seeking a Staff MLOps Engineer to oversee the release gates and operational standards for their Embodied AI model development cycles. This high-trust role ensures technical excellence across ML delivery pipelines, validating training phases and collaborating with platform teams to streamline model deployment. The position focuses on maintaining quality, safety, and efficiency in automated driving software releases.
Responsibilities
- Set and enforce standards for ML release gates and validation processes.
- Drive technical excellence across ML delivery pipelines by identifying bottlenecks.
- Collaborate with CI/CD and AI Platform teams to adapt workflows and tooling.
- Review release content including model metrics and evaluation results for quality assurance.
- Partner with evaluation teams to develop reliable methodologies and spot gaps.
Requirements
- Deep technical expertise in ML training lifecycles and model registry.
- Strong experience with PyTorch, TensorRT, quantization, and model deployment.
- Proficiency in CI/CD practices and GitHub Actions.
- Proven ability to introduce operational processes that build engineering excellence.
- Strong communication skills with a collaborative mindset.
Wayve
Greater London · GB · 750+ employees
Wayve specializes in end-to-end embodied AI solutions for autonomous vehicles, aiming to provide safe and adaptable driving intelligence. Unlike traditional systems reliant on rigid mapping, its technology learns from real-world data and simulations to operate in complex environments.
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