Responsibilities:
- 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
Required Skills:
- 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
- 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
- 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)
- Hands-on familiarity with data ingestion and processing pipelines
Preferred Skills:
- 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
Skills Required
- 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
- Strong foundation in motion control and modern neural network architectures
- Skilled in debugging robotic systems within Linux environments
- Strong programming expertise in Python and C++
- Experience model development & training with modern frameworks (e.g. PyTorch)
- Hands-on familiarity with data ingestion and processing pipelines
Plus Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Plus and has not been reviewed or approved by Plus.
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Leave & Time Off Breadth — Unlimited PTO in addition to company holidays and flexible work arrangements are offered, indicating broad time-off flexibility. This setup signals strong support for taking time away from work.
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Healthcare Strength — Tiered medical, dental, and vision options allow employees to select coverage that fits their needs. This breadth of core health coverage aligns with a comprehensive benefits approach.
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Wellbeing & Lifestyle Benefits — Daily catered lunches at key offices and company-sponsored professional development add meaningful day-to-day and growth-oriented perks. These offerings enhance overall wellbeing and workplace experience.
Plus Insights
What We Do
Plus is a global provider of highly automated driving and fully autonomous driving solutions. Named by Forbes as one of America's Best Startup Employers and Fast Company as one of the World’s Most Innovative Companies, Plus's customers are already operating its product on the road today. Working with one of the largest companies in the U.S., vehicle manufacturers and others, Plus is making transportation safer and greener. Plus has received a number of industry awards and distinctions for its transformative technology and business momentum from Fast Company, Insider, Consumer Electronics Show, AUVSI, and others. For more information, visit www.plus.ai






