Machine Learning Engineer / Software Engineer, Motion Planning Frameworks

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Woven Planet is building the safest mobility in the world. A subsidiary of Toyota, Woven Planet innovates and invests in new technologies, software, and business models that transform how we live, work and move. With a focus on automated driving, smart cities, robotics and more, we build on Toyota's legacy of trust and safety to deliver mobility solutions for all.

For nearly a century, Toyota has been delivering products and services that improve lives. Automation that originated to increase the efficiency of daily activities has evolved into the safe, reliable, connected automobiles we enjoy and depend on today. Now, we are looking to the next 100 years and to extending that dream for a better life for all people. At Woven Planet we strive to build a safer, happier, more sustainable world.

Our unique global culture weaves modern Silicon Valley innovation and time-tested Japanese quality craftsmanship. The complementary strengths enable us to optimize safety, advance clean energy, elevate well-being, and improve how people live, work, and play. We envision a human-centered future where world-class technology solutions expand global access to mobility, amplify the capabilities of drivers, and empower humanity to thrive.

About the Organization

Woven Planet is developing automated driving technology using a data-driven approach. We’re building products at autonomy levels 2-4 to drive both near- and long-term improvements to mobility for all. Woven Planet has the backing of one of the world’s largest automakers, the talent to deliver on our goal, and a built in path to product and revenue—a combination rarely seen in the mobility industry. We’re looking for doers and creative problem solvers with a passion for improving lives.

Each member of our diverse and talented group of software and hardware engineers has the opportunity to make a meaningful impact on our technology and products. Our growing team works in brand new garages and labs in Palo Alto, tests AVs at our dedicated test track in Silicon Valley, and explores the industry’s most compelling research problems at our office in London. With support from our Woven Planet colleagues in Tokyo, our work to improve the future of mobility spans the globe.

You will be interacting on a daily basis with software engineers, machine learning specialists, and researchers to tackle some of the most challenging problems in AI and robotics. We work on a diverse set of problems ranging from solving motion planning problems in challenging traffic situations, to minimizing latency on hardware accelerators, to designing novel neural network architectures.

The team is looking for a machine learning engineer expert to collaborate in the design and development of deep learning and reinforcement learning frameworks that are optimized to directly support the experimentation of novel motion planning architectures and training paradigms. As an expert in these frameworks, you will be working with the Motion Planning team to develop a machine learning first approach to motion planning that combines the latest findings in deep learning, reinforcement learning, imitation learning, motion planning, and robotics. The ideal candidate will have extensive experience building, optimizing and deploying scalable deep learning infrastructure products, will be collaborating with research teams to support novel experiments published in top-tier conferences such as NeurIPs or CVPR, and working 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 within Motion Planning
• Develop and customize deep learning frameworks and pipelines for accelerating experimentation of deep learned approaches to motion planning, taking into account many factors such as training scalability, inference speed, and the temporal/sequential nature of the problem. 
• Collaborate with team members on state-of-the-art methods that leverage imitation learning, deep reinforcement learning, and large-scale data to develop a machine learning first approach to motion planning.
• Enable training neural networks on massive volumes of sequential data, and build the necessary metrics and introspection tools to enable rapid iteration.
• Collaborate closely with the ML Infrastructure and Frameworks team to leverage and improve generalizable solutions.

Experience:

• Degree in Computer Science, Machine Learning, Robotics, or other quantitative fields or relevant work experience (MS/PhD preferred)
• Excellent programming skills in Python and C++, and experience with Pytorch or Tensorflow
• Experience with profiling and performance optimization
• Programming experience designing, implementing, or optimizing cutting-edge ML frameworks for distributed training
• (Nice to Have) Experience developing data, training or modeling infrastructure optimized for temporal/sequential modeling and/or reinforcement learning
• (Nice to Have) Experience implementing and maintaining data preprocessing and data loading libraries
• (Nice to Have) Extensive experience with distributed and cloud computing, distributed training platforms
• (Nice to Have) Experience integrating reinforcement learning libraries and/or simulation libraries in ML frameworks