Job Description
Do you enjoy applying machine learning to complex, real-world problems in autonomous vehicle testing? The Simulation Scenario Authoring team owns the formats and tools used to create synthetic simulation scenarios. We are looking for a hands-on ML Engineer to integrate, implement, and optimize our next-generation AV scenario generation workflow. This ranges from extending our AI assistant to advancing toward full scenario creation automation from natural language test specification. This role offers a unique chance to deliver immediate user impact while contributing to long-term AI-driven safety validation.
Qualifications
- MS or PhD in Computer Science, Machine Learning, or related field
- 2+ years of industry experience in Machine Learning
- Solid understanding of LLM or NLP concepts
- Proficiency in Python and ML libraries (PyTorch, NumPy) demonstrated through professional or research projects
Bonus Qualifications
- Practical experience in dataset creation for fine-tuning, system integration of ML models into production, or optimization techniques for low-latency inference systems
- Familiarity with autonomous vehicles, robotics, and/or complex simulation environments
- Hands-on experience in areas like program synthesis, diffusion models, and/or formal methods/V&V
- Relevant publications in conferences (e.g., CVPR, ICCV, RSS, and/or ICRA)
Requirements
Qualifications
- MS or PhD in Computer Science, Machine Learning, or related field
- 2+ years of industry experience in Machine Learning
- Solid understanding of LLM or NLP concepts
- Proficiency in Python and ML libraries (PyTorch, NumPy) demonstrated through professional or research projects
Bonus Qualifications
- Practical experience in dataset creation for fine-tuning, system integration of ML models into production, or optimization techniques for low-latency inference systems
- Familiarity with autonomous vehicles, robotics, and/or complex simulation environments
- Hands-on experience in areas like program synthesis, diffusion models, and/or formal methods/V&V
- Relevant publications in conferences (e.g., CVPR, ICCV, RSS, and/or ICRA)
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