Task Planning Engineer / Researcher – Robot Learning in Paris

Task Planning Engineer / Researcher – Robot Learning

Sit at the bridge between R&D and product — integrate research modules into our robots and build long-horizon task & motion planning on top of our skill-based primitives.

• Focus Robot learning task planning robotics computer vision
• About Lili-O

Our mission is to empower every person with a robot — whether that\'s an elderly person who needs a hand at home or a young professional who wants their time back.

To get there, we\'re building the brain that lets robots automate specific long-horizon tasks in unstructured, real-world environments.

That brain combines skill learning, vision, and long-horizon planning — built on a skill-based approach that decomposes complex tasks into reusable primitives.

We\'re looking for the person who will tie these building blocks together into a robust planning system.

The Role

As a Task Planning Engineer / Researcher, you\'ll sit at the bridge between R&D and product: integrating research modules into our robots and pushing forward our long-horizon task planning.

Responsibilities

• Integrate R&D modules (perception, skills, control) into the robotics stack
• Design and develop long-horizon task planning (task & motion planning)
• Build and evolve a skill orchestrator (skill composition) from our existing primitives
• Work on symbolic/hybrid reasoning (PDDL) and reactive execution (Behavior Trees)
• Collaborate with the controls team to bridge high-level planning and low-level execution (IK / MPC, whole-body control)
• Explore the integration of vision-language models (VLMs) for grounding and decision-making
• Hard Skills
• Strong experience in task planning (PDDL, symbolic or hybrid planning)
• Proficiency with Behavior Trees (BT) for orchestrating robot behaviors
• Knowledge of Vision-Language Models (VLMs) and their integration into robotics pipelines
• Solid understanding of robot control: inverse kinematics (IK), Model Predictive Control (MPC), whole-body motion control
• Experience integrating R&D modules into real-world robotic systems
• Background
• Ph D in robotics, AI, or a related field appreciated, ideally with publications
• Experience at a top-tier research lab (e. g. Deep Mind or equivalent) is a plus
• Ability to navigate between exploratory research and short-term product constraints
• Soft Skills
• Team spirit, with the ability to work closely with controls, perception, and product teams
• Autonomy and scientific rigor, paired with a strong pragmatic drive to ship
• Curiosity and a drive to push the state of the art in robot planning
• Practical Information
• Location: Paris
• Contract type: Full-time
• Compensation: Starting at €50,000 / year (depending on profile and experience)
• Work Culture — What to Expect

This role is for someone who thrives with autonomy and ambiguity, not someone who needs a roadmap handed to them every morning.

• You\'ll often work independently.

Long stretches of focused, self-directed research and integration work, with limited day-to-day supervision.

We trust you to figure out the path forward.

• Travel is part of the job.

Conferences, labs, partner visits — if there\'s a relevant event abroad, we expect you to be comfortable hopping on a plane to represent Lili-O and bring ideas back.

• Priorities shift, fast.

This is a startup.

We will sometimes change direction and de-prioritize work you\'ve put real effort into.

It\'s never personal — it\'s how startups survive.

We\'re looking for someone who can shrug it off, learn from it, and move on to the next priority without losing momentum or motivation.

• Comfort with uncertainty.

Specs, scope, and even the problem definition will evolve.

You\'ll be expected to bring structure to that ambiguity rather than wait for it to be resolved for you.

If you need a stable roadmap, a fixed scope, and predictable priorities, this probably isn\'t the right fit.

If you get energy from solving open problems, adapting on the fly, and owning your slice of the robot end-to-end — let\'s talk.

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