Principal Machine Learning Engineer – Scenario Technology
| Company | Wayve |
|---|---|
| Location | Sunnyvale, CA, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Expert or higher |
Requirements
- M.S. or Ph.D. in Computer Science, Machine Learning, or related fields, with 10+ years of industry experience.
- Proven experience in building, deploying, and scaling production-level ML solutions.
- Deep understanding of ML fundamentals with expertise in one or more areas: Deep Learning, Computer Vision, Traditional ML, or Large Language Models.
- Proficiency in Python, with a strong focus on clean, well-structured, and testable code.
- Experience leading and executing large-scale, cross-functional projects.
- 7+ years of experience in ML engineering and 4+ years in production deployments.
- Hands-on experience with data pipelines, SQL, and managing large-scale databases.
Responsibilities
- Lead the development of robust ML models and augmentation techniques that power our scenario generation and validation systems.
- Architect scalable systems for mining, curating, and synthesizing data to fuel scenario databases, using both real-world and synthetic data.
- Collaborate with cross-functional teams, including Simulation and Science, to integrate state-of-the-art technologies into our scenario pipelines.
- Drive innovation in scenario coverage, focusing on both closed-loop and open-loop evaluations.
- Own end-to-end technical solutions: from translating product requirements into engineering tasks to delivering high-impact ML models and infrastructure improvements.
- Identify and address infrastructure gaps, leading initiatives to enhance our foundational tools.
- Guide the team in navigating complex technical challenges and setting the technical direction in scenario technology.
- Mentor engineers across teams, fostering growth and innovation in scenario-driven ML engineering.
Preferred Qualifications
- + 3+ years of experience in simulation, perception, or autonomous driving technologies.
- Familiarity with training and evaluating autonomous vehicle models.
- Industry experience in autonomous vehicles, robotics, transportation, or computer vision.