Job Description
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Company Overview
A leading global technology platform is launching an autonomous vehicle-focused initiative designed to accelerate the autonomous technology ecosystem. The team brings together multidisciplinary experts to transform real-world driving operations into high-quality data that can support autonomous technology development.
The initiative focuses on addressing one of the most challenging areas in autonomous vehicles: unlocking rare, real-world, long-tail driving data. By leveraging large-scale real-world driving data, advanced machine learning, and highly scalable infrastructure, the organization aims to enable more sophisticated autonomy development and evaluation.
The Principal Machine Learning Engineer will play a critical role in advancing Physical AI by developing sophisticated autonomy algorithms and machine learning models that add rich semantic meaning to large-scale driving datasets.
The role will establish the technical direction for state-of-the-art machine learning systems focused on data mining, deep scene understanding, and causal modeling of ego vehicle behavior. The successful candidate will help architect the intelligence behind an L4 data and evaluation engine while collaborating with engineering teams across big data, compute, and cloud infrastructure.
Key Responsibilities
- Lead the strategy for developing autonomy algorithms and foundation models that extract high-fidelity semantic meaning from complex urban edge cases and enrich L4 data platforms.
- Establish the technical vision for multimodal scene understanding and causal modeling of ego vehicle behavior using logged driving data.
- Lead the design of advanced machine learning models capable of accurately interpreting complex real-world driving scenarios.
- Analyze the underlying causes of driving decisions in challenging scenarios and develop structured representations of autonomous vehicle behavior.
- Lead the creation of comprehensive taxonomies for autonomous driving datasets to enable precise data mining and edge-case discovery.
- Develop data mining capabilities that allow autonomous technology partners to query and extract highly specific driving scenarios.
- Mentor senior and lead engineers while fostering a culture of rigorous experimentation, innovation, and engineering excellence.
- Influence technical direction across multiple engineering teams.
- Collaborate across autonomous vehicle, big data, compute, and cloud engineering organizations to integrate semantic models and data evaluation platforms at scale.
- Help define and advance the technical architecture supporting L4 data and evaluation capabilities.
Required Qualifications
- 10+ years of professional experience in machine learning, robotics, autonomous systems, or a closely related field.
- Proven experience leading large-scale technical projects from initial concept through production.
- Bachelor’s degree in Computer Science, Computer Engineering, or a related technical discipline.
- Expert-level proficiency in Python and Linux environments.
- Deep expertise with modern machine learning and artificial intelligence frameworks such as PyTorch and TensorFlow.
- Strong understanding of machine learning model development, experimentation, deployment, and large-scale data processing.
- Demonstrated ability to provide technical leadership and influence engineering teams.
Preferred Qualifications
- PhD in Robotics, Machine Learning, Computer Vision, Autonomous Driving, or a related field.
- Extensive experience with C++, CUDA, and high-performance system optimization for large-scale offline datasets.
- Deep understanding of autonomous system architectures, sensor data pipelines, and offline evaluation and simulation.
- Experience developing and scaling foundation models for physical-world interaction, scene representation, or causal behavior modeling.
- Recognized expertise in machine learning or autonomous systems through patents, open-source contributions, academic publications, or other industry contributions.
- Experience working with multimodal data and advanced scene understanding techniques.
- Experience building machine learning systems designed to operate at massive scale.
- Strong understanding of autonomous vehicle data and evaluation challenges.
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