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Motional • Boston, Massachusetts, United States
Role & seniority: Internship; PhD student expected (CS/Robotics/ML/Statistics or related).
Stack/tools: Transformer-based sequence models (Trajectory Transformers, MotionLM), generative AI for motion, probabilistic ML, Conformal Prediction; Python; PyTorch or TensorFlow; ideally JAX/Flax.
Develop advanced models to learn the grammar of valid human driving for large-scale simulations.
Establish statistical safety guarantees and dynamic safety envelopes around predicted trajectories.
Benchmark traditional geometric methods vs. ML-based approaches and collaborate cross-functionally.
PhD-level background in CS/ML/Statistics or related field; strong scientific/statistical foundation.
Expertise in ML/DL with modern sequence modeling (transformers, self/cross-attention) on time-series/trajectory data.
Experience with generative AI paradigms and probabilistic ML/uncertainty quantification.
Strong Python software engineering skills; proficiency with PyTorch or TensorFlow.
Domain knowledge in autonomous vehicles (motion planning, kinematics, behavior prediction).
Familiarity with conformal prediction or distribution-free uncertainty techniques.
Experience with IRL or inferring intent; JAX/Flax and high-performance computing (jit, vmap).
Location & work type: Boston, MA; in-office days per week; internship.
The Behavior Understanding and Evaluation team at Motional is responsible for defining how we measure and validate autonomous vehicle behavior at scale. To prepare for large-scale driverless deployment, manual review and static metrics thresholds are no longer sufficient. We aim to build automated, statistically rigorous systems using cutting-edge machine learning techniques to understand and evaluate our vehicles' performance, both in real-world deployment and within simulated environments.
Determining whether a simulation "Passed" or "Failed" is a particular challenge given the multi-modal complexity of real-world human driving.This is a research-forward engineering role focused on helping us build a Next-Generation Semantic Validator: a production-grade machine learning evaluation system that learns the distribution of valid human driving behavior and uses it as a "Safety Ruler" for autonomous vehicle releases.
This internship is based in our Boston office and requires in-office days each week.
Develop Advanced Models: Leverage Transformer-based Generative Models (e.g., Trajectory Transformers or MotionLM architectures) to learn the 'grammar' of valid human driving knowing ground truth of the past and future, allowing rapid assessment of large scale simulations that may otherwise require a human in the loop
Establish Statistical Safety Guarantees: Pioneer the definition and implementation of key evaluation metrics using techniques such as Conformal Prediction to establish rigorous, dynamic safety envelopes around predicted trajectories.
Benchmark Methods: Own the benchmarking initiative comparing traditional geometric methods (e.g., single trajectory comparison) against cutting-edge generative / ML based approaches to prove a reduction in "False Fails."
Collaborate: Partner cross-functionally with Behaviors, Actions, Simulation, System Engineering, and Research to share insights on multi-modal truth and probabilistic safety.
Currently a PhD in Computer Science, Robotics, Machine Learning, Statistics, or a related field. Strong foundation in scientific and statistical methodologies. Expertise in Machine Learning and Deep Learning, specifically modern Sequence Modeling (Transformers, Self-Attention, and Cross-Attention applied to time-series or trajectory data). Hands-on experience with Generative AI paradigms (e.g., treating motion as a generative next-token prediction task, or using Diffusion Models). Strong grasp of Probabilistic ML and uncertainty quantification to distinguish between "rare but safe" behaviors and out-of-distribution failures. Strong software engineering skills in Python and standard deep learning frameworks (PyTorch or TensorFlow).
Domain knowledge in Autonomous Vehicles, specifically Motion Planning, Kinematics, or Behavior Prediction. Familiarity with Conformal Prediction or other distribution-free uncertainty quantification techniques. Experience with Inverse Reinforcement Learning (IRL) or inferring intent from human driving. Experience with JAX / Flax and composable function transformations (jit, vmap) for high-performance computing.
Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more.
Our journey is always people first.
We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move.
Higher purpose, greater impact.
We’re creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it’s not only good for our business, it’s the right thing to do.
Scale up, not starting up.
Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. We’re driven to scale; we’re moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges.
Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. Headquartered in Boston, Motional has operations in the U.S and Asia. For more information, visit www.Motional.com and follow us on Twitter, LinkedIn, Instagram and YouTube.
Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E-Verify. All newly-hired employees are queried through this electronic system established by the DHS and the SSA to verify their identity and employment eligibility.