Production ML Engineer – Autonomous Driving

Wayve; United States; Hybrid; USD 170,000 - 230,000; Full time

Benefits offered by this job

Equity

Relocation & visa sponsorship

Hybrid working model

Health insurance and benefits

Job summary

Wayve is seeking an ML Engineer to design and deliver ML-driven behaviours for autonomous driving. You’ll work across model architecture, data pipelines, evaluation and deployment, collaborating with AI Platform, Simulation, Robot SW and Model Release teams to build scalable systems.

You’ll lead technical initiatives, mentor engineers, and contribute to long-term direction across Autonomy, with hybrid working across multiple hubs and a strong focus on real-world deployment.

Qualifications

• Extensive track record shipping DL systems to production.
• Expert in deep learning (sequential models, control, planning, or perception).
• Proficient in Python and other languages (C++, CUDA) and ML frameworks (PyTorch).
• Experience with real-time systems or robotics, ideally with simulation or vehicle-in-the-loop.
• Ability to lead technical initiatives across teams and mentor engineers.

Responsibilities

• Design and deliver ML-driven behaviours for assisted to autonomous driving.
• Develop architecture, data pipelines, evaluation frameworks, and deployment.
• Collaborate with AI Platform, Simulation, Robot SW and Model Release teams.
• Build end-to-end driving models with state-of-the-art performance.
• Mentor engineers and shape long-term technical direction across Autonomy.

Skills

Deep learning systems

Python

C++

CUDA

PyTorch

Real-time / robotics

Leadership & mentoring

Tools

PyTorch

Python

C++

CUDA

Job description

Wayve is seeking an ML Engineer to design and deliver ML-driven behaviours for autonomous driving. You’ll work across model architecture, data pipelines, evaluation and deployment, collaborating with AI Platform, Simulation, Robot SW and Model Release teams to build scalable systems.

You’ll lead technical initiatives, mentor engineers, and contribute to long-term direction across Autonomy, with hybrid working across multiple hubs and a strong focus on real-world deployment.