Senior Motion Planning Engineer Job at Einride AB in Brockport
Einride AB, Brockport, NY, United States
Want to be part of transforming road freight – for good? Einride is showing the world a new way to move, based on the latest digital, electric and autonomous technologies. Through freight capacity as a service, we enable businesses around the world to accelerate towards their sustainability goals.
Founded in 2016, Einride became the first company in the world to deploy a cab-less autonomous electric vehicle on a public road (Sweden, 2019). In 2022, we were the first to successfully operate such a vehicle on a US public road. Today our award‑winning technology has been launched across 8 countries (and counting). Our clients are some of the world’s biggest shippers, including Fortune 500 companies. We are also operating Sweden’s largest truck dedicated public charging network and counting.
We are growing our team and looking for a skilled and passionate Senior Motion Planning Engineer to join our Motion Planning and Control team. The ideal candidate brings strong experience in optimization‑based and sampling‑based planning methods, solid software engineering skills, and the ability to bridge the gap between research and production‑grade systems.
You will: Design, develop, and maintain motion planning and control algorithms for autonomous heavy‑duty vehicles — including trajectory optimization, path planning, real‑time re‑planning, and lateral/longitudinal control using optimization‑based and sampling‑based methods (e.g. MPC, MPPI)
Ensure planning safety through the design and application of safety‑aware methods such as tube MPC, control barrier functions, or similar constraint‑based approaches
Work with semantic maps and map‑based representations that inform planning and prediction — including lane‑level geometry, traffic rules, and operational boundaries
Collaborate with engineers across Perception, Control, and Systems Engineering to ensure the planning stack operates reliably within the full autonomy pipeline, and evaluate learning‑based planning approaches where they can improve performance
Optimize critical components for real‑time performance, including GPU‑accelerated workloads, and contribute to simulation, testing, and validation from desktop through to on‑vehicle deployment
We expect you to: PhD or highly qualified MSc in Robotics, Control Systems, Vehicle Engineering, Computer Science, Applied Mathematics, or a related field; 3+ years of research or industry experience in motion planning, trajectory optimization, or vehicle control for autonomous systems
Solid experience with optimization‑based planning (e.g. MPC, convex
on‑linear optimization) and/or sampling‑based planning (e.g. MPPI, CEM), with a strong foundation in optimization, numerical methods, and control theory
Proficiency in Go/Golang, C++ and Python; experience with CUDA or GPU‑accelerated computing is a strong plus
Familiarity with adjacent domains such as prediction/forecasting, behavioral/decision‑level planning, low‑level vehicle control, or functional safety
Knowledge of vehicle dynamics modeling (e.g. bicycle models, multi‑body dynamics, tire‑force models) is a plus
Experience with safety‑critical planning methods (tube MPC, control barrier functions, reachability analysis) or learning‑based planning/control (imitation learning, reinforcement learning) is a plus
This is a position based in Stockholm or Gothenburg. You will be part of a truly diverse, high‑performing team with a common passion for sustainability and making things happen in an innovative way.
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Founded in 2016, Einride became the first company in the world to deploy a cab-less autonomous electric vehicle on a public road (Sweden, 2019). In 2022, we were the first to successfully operate such a vehicle on a US public road. Today our award‑winning technology has been launched across 8 countries (and counting). Our clients are some of the world’s biggest shippers, including Fortune 500 companies. We are also operating Sweden’s largest truck dedicated public charging network and counting.
We are growing our team and looking for a skilled and passionate Senior Motion Planning Engineer to join our Motion Planning and Control team. The ideal candidate brings strong experience in optimization‑based and sampling‑based planning methods, solid software engineering skills, and the ability to bridge the gap between research and production‑grade systems.
You will: Design, develop, and maintain motion planning and control algorithms for autonomous heavy‑duty vehicles — including trajectory optimization, path planning, real‑time re‑planning, and lateral/longitudinal control using optimization‑based and sampling‑based methods (e.g. MPC, MPPI)
Ensure planning safety through the design and application of safety‑aware methods such as tube MPC, control barrier functions, or similar constraint‑based approaches
Work with semantic maps and map‑based representations that inform planning and prediction — including lane‑level geometry, traffic rules, and operational boundaries
Collaborate with engineers across Perception, Control, and Systems Engineering to ensure the planning stack operates reliably within the full autonomy pipeline, and evaluate learning‑based planning approaches where they can improve performance
Optimize critical components for real‑time performance, including GPU‑accelerated workloads, and contribute to simulation, testing, and validation from desktop through to on‑vehicle deployment
We expect you to: PhD or highly qualified MSc in Robotics, Control Systems, Vehicle Engineering, Computer Science, Applied Mathematics, or a related field; 3+ years of research or industry experience in motion planning, trajectory optimization, or vehicle control for autonomous systems
Solid experience with optimization‑based planning (e.g. MPC, convex
on‑linear optimization) and/or sampling‑based planning (e.g. MPPI, CEM), with a strong foundation in optimization, numerical methods, and control theory
Proficiency in Go/Golang, C++ and Python; experience with CUDA or GPU‑accelerated computing is a strong plus
Familiarity with adjacent domains such as prediction/forecasting, behavioral/decision‑level planning, low‑level vehicle control, or functional safety
Knowledge of vehicle dynamics modeling (e.g. bicycle models, multi‑body dynamics, tire‑force models) is a plus
Experience with safety‑critical planning methods (tube MPC, control barrier functions, reachability analysis) or learning‑based planning/control (imitation learning, reinforcement learning) is a plus
This is a position based in Stockholm or Gothenburg. You will be part of a truly diverse, high‑performing team with a common passion for sustainability and making things happen in an innovative way.
#J-18808-Ljbffr