Role SummaryWe are seeking a highly motivated Senior Controls Modeling and Simulation Engineer to join our Autonomy Controls team. The ideal candidate will have a strong background in motion planning, control systems, and vehicle dynamics, with experience in both modeling and simulation. You will play a key role in developing and improving advanced control algorithms and supporting a variety of projects critical to our next generation autonomous vehicle systems.
Responsibilities- Design and implement high-performance control algorithms to solve real-time trajectory optimization and control problems.
- Design and implement optimization-based control strategies using convex and non-convex optimization techniques to solve control problems
- Collaborate with cross-functional teams to deliver improvements in vehicle motion planning, control, and feature engineering for L3/L4 autonomy.
- Optimize computational efficiency of control algorithms for real-time embedded implementation, including algorithm complexity reduction and solver selection.
- Support data analysis, KPI development, and embedded software integration for control features.
- Build and refine vehicle dynamics models to support simulation and validation of control strategies.
- Assist in the creation and maintenance of simulation environments for vehicle dynamics and control system validation.
- Contribute to the development and validation of new features through on-road and simulation-based testing.
Qualifications- Bachelor's or Master's degree in Mechanical Engineering, Electrical Engineering, Robotics, Computer Science, or a related field.
- Strong background in motion planning, control theory, and vehicle dynamics.
- 3+ years of experience in optimization theory and its application to control systems design and proficiency with optimization solvers and frameworks.
- Proficiency in programming languages such as C++ and Python.
- Knowledge of optimization algorithms including gradient descent, interior point methods, and sequential convex programming.
- Familiarity with multi-objective optimization and trade-off analysis for competing control objectives (comfort, safety, efficiency).
- Strong background in machine learning, AI, and robotics, with hands-on experience developing and deploying learning-based algorithms.
- Experience with embedded systems and real-time control implementation is a plus.
- Excellent problem-solving skills and ability to work collaboratively in a fast-paced environment.
- Prior internship or project
Pay DisclosureThe salary range for this role is $179,000.00 - $223,800.00 for California based applicants. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee's position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, geographic location, shift, and organizational needs.
The successful candidate may be eligible for annual performance bonus and equity awards.
We offer a comprehensive package of benefits for full-time and part-time employees, their spouse or domestic partner, and children up to age 26, including but not limited to paid vacation, paid sick leave, and a competitive portfolio of insurance benefits including life, medical, dental, vision, short-term disability insurance, and long-term disability insurance to eligible employees. You may also have the opportunity to participate in Rivian's 401(k) Plan and Employee Stock Purchase Program if you meet certain eligibility requirements. Full-time employee coverage is effective on their first day of employment. Part-time employee coverage is effective the first of the month following 90 days of employment. More information about benefits is available at rivianbenefits.com.
You can apply for this role through careers.rivian.com (or through internal-careers-rivian.icims.com if you are a current employee). This job is not expected to be closed any sooner than July 20, 2026.
