Machine Learning Engineer, App SW

Wayve

$283K — $381K *
Transportation
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Extensive proven experience in deploying deep learning systems.
  • Expertise in deep learning, particularly with sequential models and perception.
  • Proficient in Python, C++, CUDA, and relevant ML frameworks like PyTorch.
  • Background in real-time systems or robotics, preferably involving simulations.
  • Strong leadership skills with the capability to guide and mentor teams.

Responsibilities

  • Develop and enhance end-to-end models for optimal driving performance.
  • Lead initiatives on personalized and collaborative driving behaviors.
  • Build robust evaluation frameworks for driving performance metrics.
  • Curate data for improved scenario diversity and feature development.
  • Influence architectural and methodological decisions for scalable systems.
  • Collaborate with cross-functional teams to maintain integration and speed of iteration.
  • Mentor engineers and shape the long-term vision for autonomy.

Benefits

  • Hybrid working policy that balances office and remote work.
  • Flexible core working hours for better work-life integration.
  • Opportunities for innovation and collaboration in dynamic team settings.
Full Job Description
The Role

As an ML Engineer within the Application Engineering team, you'll lead critical initiatives that push the frontier of model-based autonomous driving-both in terms of core driving performance and feature-level intelligence such as personalisation, comfort, and collaboration.

You'll design and deliver ML-driven behaviors that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You'll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production.
Responsibilities:
  • Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization.
  • Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment.
  • Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness.
  • Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development.
  • Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems.
  • Collaborate cross-functionally across various teams to ensure integration and iteration velocity.
  • Mentor senior engineers and shape the long-term technical direction across Autonomy.
About you:

In order to set you up for success as a Machine Learning Engineer at Wayve, we're looking for the following skills and experience.
Essential
  • Extensive and proven track record of shipping deep learning systems to production.
  • Expert in deep learning (esp. sequential models, control, planning, or perception).
  • Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.
  • Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components.
  • Ability to lead technical initiatives across teams, drive alignment, and mentor engineers.
Desirable
  • Prior work in autonomous driving, imitation learning, or trajectory prediction.
  • Familiarity with personalization, human behavior modeling, or driver intent inference.
  • Experience integrating ML systems into production hardware or multi-agent simulation.

This role is a full-time role based in Sunnyvale or Detroit (hybrid) and the reasonably estimated salary for this role ranges from $283,500 to $381,600, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.

At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We operate core working hours so you can determine the schedule that works best for you and your team.

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