Senior Planning & Prediction Engineer

Inceptio Technology; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Inceptio Technology is building full-stack Level 4 autonomous driving systems for heavy‑duty trucks, deploying on highway and trunk logistics. This overseas planning team role is high ownership and shapes the prediction, decision, and motion‑planning stack for our global product.

Heavy trucks require ahead-thinking planning, considering physics, stability, and fuel efficiency as you scale to real freight lanes. Join to lead end-to-end planning with safety at the core.

Qualifications

• Strong fundamentals in motion planning using search, sampling, or optimization methods.
• Experience with behavior/decision-making (state machines, POMDPs, or learning-based policies).
• Hands-on end-to-end/learned planning experience and examples.
• Solid grasp of vehicle kinematics and dynamics and their constraints.

Responsibilities

• Architect and develop the prediction, behavior, and motion-planning stack for L4 highway trucking.
• Build multi-agent trajectory prediction and interaction modeling for surrounding traffic.
• Advance toward learned and end-to-end planning, including data-driven decisions.
• Design highway maneuver planning: lane changes, merges, ramps, gap selection.
• Develop planning that respects heavy-truck kinematics while balancing safety and efficiency.
• Train, optimize and deploy models on automotive-grade compute under real-time constraints.

Skills

Motion planning

C++

Python

End-to-end planning

Prediction/behavior modeling

Multi-agent planning

Education

MS or PhD in CS/Robotics/EE/Applied Math

Tools

NVIDIA DRIVE Orin/Thor

Job description

Senior Planning & Prediction Engineer — Autonomous Driving (L4 Heavy-Duty Trucks)

Team: Autonomous Driving · Planning & Prediction

Employment type: Full-time

About the Role

We are building full-stack Level 4 autonomous driving systems for heavy-duty trucks in highway and trunk logistics, and we are standing up our overseas planning team. This is an early, high-ownership role: you will help architect the prediction, decision, and motion-planning stack for our global product.

Heavy trucks are not passenger cars. A loaded tractor-trailer has long stopping distances, limited acceleration, articulated dynamics, and real rollover and jackknife limits — so planning has to think much further ahead and respect physics that a robotaxi can ignore. If you want to make high-stakes decisions in dense highway traffic, and see them run on real freight lanes at scale, this is the place.

What You’ll Do

• Architect and develop the prediction, behavior, and motion-planning stack for L4 highway trucking.
• Build multi-agent trajectory prediction and intent/interaction modeling for surrounding traffic.
• Drive the stack toward learned and end-to-end planning — including joint prediction-planning and data-driven decision-making — and help lead the shift from modular pipelines toward end-to-end autonomous driving.
• Design behavior and decision-making for highway maneuvers: lane keeping and changes, merges, on/off ramps, gap selection, and interaction with cut-ins and merging vehicles.
• Develop motion planning and trajectory optimization that respect heavy-truck kinematics and dynamics (mass, articulation, stability limits) while balancing safety, smoothness, and efficiency.
• Account for fuel/energy efficiency in planning — predictive, eco-driving behavior over highway terrain.
• Train, optimize, and deploy models on automotive-grade compute (e.g., NVIDIA DRIVE Orin/Thor) under hard real-time constraints.
• Tackle highway long-tail decision scenarios and build planning with safety in mind, working with our safety team where relevant.
• Collaborate closely with perception, control, mapping, data, and platform teams.

What We’re Looking For

• MS or PhD in Computer Science, Robotics, EE, Controls, Applied Math, or equivalent practical experience.
• Strong fundamentals in motion planning — search-, sampling-, or optimization-based methods (e.g., A*/lattice planners, RRT, iLQR, MPC-based planning).
• Experience with behavior/decision-making (state machines, behavior trees, POMDPs, or learning-based decision policies).
• Experience with trajectory prediction and multi-agent/interaction-aware modeling.
• Hands-on end-to-end/learned planning background. Direct experience with learned or end-to-end planning and prediction — e.g., imitation- or RL-based planning, learned cost models, transformer-based motion planning, or joint prediction‑planning.
• Solid grasp of vehicle kinematics and dynamics and how they constrain feasible trajectories.
• Proficient in C++ and Python, comfortable working under real-time constraints.

Nice to Have

• Production autonomous-driving/ADAS experience, ideally shipped to vehicles.
• Commercial-vehicle or heavy-truck AV experience.
• Deep-learning prediction or planning (transformers, graph networks, learned cost models).
• Optimization and numerical methods (QP/NLP solvers, real-time optimization).
• Eco-driving/predictive energy-efficient planning.
• NVIDIA DRIVE platform (Orin/Thor) and embedded experience.
• Familiarity with functional safety (ISO 26262) or SOTIF (ISO 21448).

Why Join Us

• L4, not L2 — full self-driving for commercial trucking, not driver‑assist.
• Real deployment at scale, with a fast feedback loop from road to model.
• A global, founding-stage team where your architectural decisions set the direction.