Principal Machine Learning Engineer, Geometric Vision

1 нед. назад

USALead

deep learningcomputer visionrobotics3d modeling

Lead development of geometric vision and 3D foundation models for autonomous driving systems using deep learning and robotics.

О компании

Founded in 2017, is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.
Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.  In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.
At , your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  
Make the experience that defines your career!

Обязанности

As a Principal Engineer on the Model Foundations team you will  build the geometric vision and 3D foundation models that underpin our autonomous driving systems.You will work at the intersection of large-scale deep learning, geometric computer vision, and real-world robotics, developing models that learn 3D structure and dynamics from fleet-scale sensor data.
You will be a hands-on technical leader. You will set direction for geometric vision, prototype and train new model architectures, build the data and supervision needed to scale them, and take successful ideas through to deployment on real vehicles.
Design and train 3D foundation models and world models using large-scale driving data.
Develop model architectures for 3D perception, geometric reasoning, reconstruction, and world modeling across space and time.
Build scalable data generation and auto-labeling pipelines that produce high-quality geometric supervision from large volumes of sensor data.
Develop and scale offline SLAM and 3D reconstruction systems and pipelines, using large-scale sensor data to recover accurate trajectories, scene geometry, calibration signals, and geometric supervision for model training and evaluation.
Develop and apply techniques in multi-view geometry, neural rendering, NeRFs, Gaussian Splatting, implicit 3D representations, and feedforward 3D modeling.
Explore geometry-aware tokenization and representation learning, including efficient ways to encode and fuse information across cameras, viewpoints, time, and sensing modalities.
Develop foundation vision models that make effective use of camera, radar, LiDAR, and other sensor data for learning rich representations of the physical world.
Explore video and generative modeling approaches for learning scene structure, dynamics, and future evolution from driving data.
Train and evaluate models at scale on distributed compute, rapidly iterating on architectures, objectives, data, and training recipes.
Develop automated evaluation and ground-truth systems for measuring geometric consistency, reconstruction quality, 3D understanding, and downstream driving performance.
Optimize and deploy models into production autonomous-driving systems, working across model architecture, inference, and onboard constraints.
Set technical direction for geometric vision at and work closely with researchers and engineers across foundation models, perception, simulation, data, sensing, and deployment.

Требования

In order to set you up for success as a Principal Machine Learning Engineer, Geometric Vision at , we’re looking for the following skills and experience.  
Essential  
Deep expertise in 3D computer vision, geometric vision, or 3D machine learning, with experience in areas such as multi-view geometry, neural rendering, reconstruction, implicit representations, or world modeling.
Strong experience designing, training, and evaluating modern deep-learning models at scale, using PyTorch or a comparable framework.
Strong mathematical and technical foundations in geometry, linear algebra, probability, optimization, and 3D transformations, combined with excellent software engineering skills in Python and C++
A track record of taking difficult research problems from idea to working system, including building large-scale data, training, evaluation, or deployment pipelines.
Principal-level technical leadership: the ability to identify high-leverage problems, set research and engineering direction, make strong architectural decisions, and raise the technical bar across teams.
Desirable  
3D and geometric vision: multi-view geometry, dense 3D reconstruction, neural fields, NeRFs, Gaussian Splatting, or feedforward 3D models.
Foundation and world models: large-scale vision pre-training, self-supervised learning, video models, generative models, or learned scene dynamics.
Geometric data engines: offline SLAM, structure-from-motion, reconstruction, calibration, auto-labeling, and large-scale ground-truth generation.
Multimodal perception: learned representations and fusion across camera, radar, LiDAR, and other sensing modalities.
Production ML systems: distributed training, large-scale experimentation, and deploying neural networks on real-time, resource-constrained hardware.
This is a full-time role based in our office in Sunnyvale.  At 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. The reasonably estimated salary for this role ranges from $ 407,330 to $ 460,020 plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.
is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know.
We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply. At we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition  (including breastfeeding) or any other basis as protected by applicable law.  
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DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.