Asks for

The ad says“Hands-on application skills in any of the following areas: adaptive and nonlinear control, MPC & optimal control, robust control, data-driven control, Kalman filters, etc”
The ad says“Proven expertise with application, verification and validation for ADAS/autonomous driving features and functions”
The ad says“Have a solid understanding of AV control, vehicle dynamics and drive-by-wire systems”
The ad says“Skilled in debugging robotic systems within Linux environments, with strong programming expertise in Python and C++”
The ad says“Hands-on familiarity with data ingestion and processing pipelines”
The ad says“Hands-on application skills in any of the following areas: adaptive and nonlinear control, MPC & optimal control, robust control, data-driven control, Kalman filters, etc”
The ad says“Have a solid understanding of AV control, vehicle dynamics and drive-by-wire systems”
The ad says“Strong foundation in motion control and modern neural network architectures, with expertise in at least one application area, such as IL/RL, time-series analysis, or dynamic system modeling”
The ad says“Hands-on application skills in any of the following areas: adaptive and nonlinear control, MPC & optimal control, robust control, data-driven control, Kalman filters, etc”
The ad says“Skilled in debugging robotic systems within Linux environments, with strong programming expertise in Python and C++”
The ad says“Your responsibilities will include the development of machine-learning vehicle models and learning based control policies leveraging the extensive data we collect every day across our autonomous trucking fleet”
The ad says“Strong foundation in motion control and modern neural network architectures, with expertise in at least one application area, such as IL/RL, time-series analysis, or dynamic system modeling”
The ad says“Hands-on application skills in any of the following areas: adaptive and nonlinear control, MPC & optimal control, robust control, data-driven control, Kalman filters, etc”
The ad says“Strong foundation in motion control and modern neural network architectures, with expertise in at least one application area, such as IL/RL, time-series analysis, or dynamic system modeling”
The ad says“Hands-on application skills in any of the following areas: adaptive and nonlinear control, MPC & optimal control, robust control, data-driven control, Kalman filters, etc”
The ad says“Hands-on application skills in any of the following areas: adaptive and nonlinear control, MPC & optimal control, robust control, data-driven control, Kalman filters, etc”
The ad says“Skilled in debugging robotic systems within Linux environments, with strong programming expertise in Python and C++”
The ad says“Experience model development & training with modern frameworks (e.g. PyTorch)”
The ad says“Hands-on application skills in any of the following areas: adaptive and nonlinear control, MPC & optimal control, robust control, data-driven control, Kalman filters, etc”
The ad says“Strong foundation in motion control and modern neural network architectures, with expertise in at least one application area, such as IL/RL, time-series analysis, or dynamic system modeling”
The ad says“Have a solid understanding of AV control, vehicle dynamics and drive-by-wire systems”

As a Research Engineer, you will deliver mission-critical improvements and new features for our autonomy motion planning and control stack
You will be a crucial part of our team, working alongside engineers, research scientists, and domain experts to build optimal and data driven controls to realize planned vehicle trajectories
Your responsibilities will include the development of machine-learning vehicle models and learning based control policies leveraging the extensive data we collect every day across our autonomous trucking fleet
You will also have the opportunity to solve real-world autonomy system challenges by participating in vehicle performance analysis, tuning, and troubleshooting. You will contribute significantly to our commitment to pushing the frontiers of technological innovation in Autonomy
Design, implement, and enhance control algorithms by developing frameworks that integrate MPC with learning based approaches (DL/RL/IL)
Work cross-functionally with domain experts to implement data driven controller design for scalability
Develop tools and infrastructure for dataset generation, training, and evaluation to drive advancements in online control optimization
Ensure all model development keeps a real-time focus and operates efficiently in compute-constrained environments
Take a lead role in the planning and execution of vehicle testing in the offline simulation environment and on the public road to systematically improve performance, as well as performing root cause analysis and debugging to address the issues
Track and incorporate the latest research advancements

Benefits

Unlimited PTO: Unlimited paid time off on top of company holidays to recharge and enjoy as needed
Flexible working: Flexibile work arrangements and technology to help manage life’s demands at the office and remote
Health benefits: Tiered options in medical, dental, and vision insurance to fit your unique needs
Future building: Equity, 401k and FSA options for all eligible employees to help build for your future
Professional development: Company-sponsored professional development opportunities such as conferences, training, and workshops
Catered lunches: Daily catered lunches at both of our Santa Clara and Fremont offices

Preferred experience

Master’s or PhD degree in Computer Science, Mechanical Engineering, Robotics, Aerospace Engineering or related field
2+ years of MLE experience or industry experience designing and developing for robotics applications
Skilled in debugging robotic systems within Linux environments, with strong programming expertise in Python and C++
Experience model development & training with modern frameworks (e.g. PyTorch)
Strong foundation in motion control and modern neural network architectures, with expertise in at least one application area, such as IL/RL, time-series analysis, or dynamic system modeling
Hands-on familiarity with data ingestion and processing pipelines
Hands-on application skills in any of the following areas: adaptive and nonlinear control, MPC & optimal control, robust control, data-driven control, Kalman filters, etc
Have a solid understanding of AV control, vehicle dynamics and drive-by-wire systems
Proven expertise with application, verification and validation for ADAS/autonomous driving features and functions