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Salary
≈ $121k – $233k per year (Estimated)
Location
In office (Northbrook)
Seniority
Senior
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 29, 2026. First seen by Alion on Oct 29, 2026.

Overview
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Latest News With Lightspeed you have access to the best technology solutions from telecom, cloud, data storage, cybersecurity and the latest digital technology.

About LightSpeed

LightSpeed Build Technologies is revolutionizing the construction industry through AI-powered robotics. Our flagship systems-BRUTE for automated wall panel manufacturing and DEX for on-site collaborative construction-are addressing the global housing crisis by delivering unprecedented speed, precision, and affordability in homebuilding.

Position Overview

As Senior Agentic Planning Engineer, you will own how LightSpeed's robots decide what to do next. Every robot action is one of two primitives, move or actuate. You will build the planner that reasons over a goal, sequences those primitives, chooses classical control or a learned policy for each step, and recovers when a step fails.

Planning doesn't stop after the first step. Your planner keeps deciding as the robot works, using live data from the sensors and the result of every action. When it works, a robot finishes a whole panel or room on its own, with every decision it makes.

What you'll work on

Primitives and contracts

  • Define primitives and their contract: inputs, preconditions, success checks and failure codes

  • Define the interface between the planner and the executor: step request, choice of classical or policy, result and evidence

  • Keep the primitive library callable by the planner, not wired by hand for each task

Planning and reasoning

  • Build the task graph from BIM: the elements in front of the robot and the dependencies between them

  • Build the planner in two layers: a reasoning layer for open-ended decisions, and solvers for reach, collision and trajectory

  • Decide when to use a reasoning model and when not to; keep it off the real-time control path

  • Set the rule for choosing classical control or a learned policy for each step, based on world-model confidence and how structured the step is

  • Extend the planner's reasoning and sequencing for each new trade

  • Measure success by how fast a new trade can be taught

Execution and recovery

  • Build the execution monitor: verify each step, then retry, switch executor, skip and flag, or escalate to a person

  • Log every decision with its reason and evidence, so any outcome can be explained

  • Keep the planner inside guardrails: a constrained action space, declared parameter ranges, and safety that is never the planner's to decide

  • Build the operator view for escalations and remote assistance

Multi-robot readiness

  • Design the planner so two robots can share one world model and divide work: one holds, one fastens; one supplies, one installs

Foundation and skill pipeline

  • Co-own the foundation interfaces with the world sensing and robot learning principals, and own the primitive contract

  • Run the new stack alongside production on our existing robots, switchable per job, so it is tested on real work from the start

  • Prove the system by running whole tasks end to end on real robots, with recovery

Technical leadership

  • Set planning and autonomy architecture across both robot families

  • Mentor autonomy and motion planning engineers

  • Make the system something engineers can teach new trades with, without you in the loop

  • Stay hands-on: write production code and test on real robots every week

Required qualifications

Autonomy and planning

  • 5+ years building autonomy, task planning or decision-making systems for robots or autonomous vehicles, with systems deployed in production

  • Task-level planning on real robots: behavior trees, task and motion planning (TAMP), HTN or similar

  • Execution monitoring and fault recovery in a deployed system

  • Built interfaces between planning, perception and control that other teams built against

  • Hands-on with reasoning models or LLMs in a decision loop, and a clear view of where they help and where they don't

Engineering and leadership

  • Strong Python and C++

  • Experience with ROS2, Linux, Docker and CI/CD workflows

  • A record of setting technical direction across teams and mentoring senior engineers

Preferred qualifications

  • MS or PhD in Robotics, Computer Science, AI or a related field

  • Behavior or decision planning on an autonomous vehicle team

  • LLM or VLM agents for robotics, with constrained action spaces and guardrails

  • Motion planning and trajectory optimization: MoveIt, OMPL or similar

  • Multi-robot coordination, task allocation or fleet orchestration

  • Functional safety or formal verification for autonomous systems

  • Planning from CAD or BIM models

  • Background in construction, manufacturing or industrial automation

Why Join LightSpeed

  • Build the decision-making of robots that are addressing the housing crisis

  • Own autonomy end to end, from the primitive contract to whole tasks finished on their own

  • Shape the foundation every trade is built on

  • Work on rare real-world problems: agentic planning on physical robots, recovery in messy sites, two robots sharing one task

  • Hands-on culture with direct access to robots, sensors and manufacturing environments

  • Competitive compensation and comprehensive benefits

Employment Relationship

This position is at-will, meaning either you or the Company may terminate employment at any time, with or without cause or notice.

Equal Opportunity

LightSpeed Build Technologies is an equal opportunity employer committed to building a diverse and inclusive workplace. We welcome candidates from all backgrounds and experiences.

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