AI/ML Job: Deep Learning Engineer, Autonomy

Deep Learning Engineer, Autonomy at Level 5

🇺🇸 United States › California › Palo Alto   (Posted Jan 18 2022)

Job description
Level 5, part of Woven Planet, is developing self-driving technology using a machine-learned approach to create safe mobility for everyone. Our goal is to build level 4 autonomous vehicles to improve personal transportation on a global scale. Woven Planet is a software-first subsidiary of Toyota whose vision is to create mobility of people, goods, and information that everyone can enjoy and trust.

As part of Woven Planet, Level 5 has the backing of one of the world’s largest automakers, the talent to deliver on our goal, and the opportunity for near-term product impact and revenue—a combination rarely seen in the AV industry.

Level 5 is looking for doers and creative problem solvers to join us in improving mobility for everyone with self-driving technology. We’ve built a diverse and talented group of software and hardware engineers, and each has the opportunity to make a meaningful impact on our self-driving stack.

Our team of more than 300 works in brand new garages and labs in Palo Alto, tests AVs at our dedicated test track in the Silicon Valley, and explores the AV industry’s most compelling research problems at our office in London. With support from more than 800 Woven Planet colleagues in Tokyo, Level 5’s work to improve the future of mobility spans the globe. And we’re moving fast — in Level 5’s first 18 months, we launched an employee pilot, and are now testing our fourth generation vehicle platform in San Francisco. Learn more at level-5.global.

You will be interacting on a daily basis with other software engineers and researchers to tackle some of the most challenging problems in AI, robotics, and computer vision. We work on a diverse set of problems ranging from solving optimization problems in 3D geometric computer vision, to minimizing latency on hardware accelerators, to designing novel neural network architectures.

The team is looking for a deep learning expert to drive the research and deployment of the next generation of deep learning models for Autonomous driving. The ideal candidate will have published some deep learning research in top-tier conferences such as NeurIPs or CVPR, built and deployed real-world deep learning products and worked in a fast-paced environment along with other highly talented engineers. We recognize the unique capabilities each team member can bring, though and encourage applicants to reach out even if they do not match all of the characteristics described below.

Responsibilities:
Work in a small, high-velocity team of engineers and researchers
Develop a vision for the next generation of deep learning systems for advanced driving systems and autonomous vehicles, taking into account many factors such as scalability, inference speed, and generalization power
Employ self-supervised, end-to-end learned methods and active learning while leveraging massive amounts of fleet data
Be a champion of the scientific method and critical thinking in inventing state-of-the-art deep learning solutions but is also a leader in applying rigorous engineering practices during validation and deployment
Collaborate closely with teams such as Planning, Simulation, Infrastructure, Tooling, and Hardware to drive a unified vision and roadmap
Advance the state-of-the-art and represent Level 5 at top-tier conferences (e.g. CVPR, NeurIPs, ICCV, CoRL, ICRA)

Experience:
MS (PhD preferred) in Computer Vision, Machine Learning, Robotics, or other quantitative fields or relevant work experience
Expertise in building deep learning models and systems with Python and C++
Programming experience implementing cutting-edge deep learned ML solutions using PyTorch / Tensorflow
Understanding of ML workflow: preparing the data, implementing and training ML models, evaluating results, running ablation studies, deploying inference on different platforms
(Nice to have) Experience with quantization-aware training and low-precision inference
(Nice to have) Experience targeting power-efficient, edge-compute architectures
(Nice to have) Experience working on self-driving problems (Perception, Prediction, Mapping, Localization, Planning, Simulation)
(Nice to have) Expertise (MS or PhD-level) in probabilistic modeling, high performance compute, dynamical systems, or computational geometry

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