Hiring nowReasoning, planning and search

Planning Algorithms Researcher

Also called AI Planning Scientist

A Planning Algorithms Researcher builds planners that choose sequences of actions under hard constraints. Companies hire them so agents and systems can complete multi-step tasks reliably, within limits on time, cost and resources.

What they do

Plans are only useful if they hold up against real constraints. The researcher chooses a planning representation, builds or adapts a search method, and tests it in simulated environments. Some days go on heuristics and pruning. Others go on checking why a plan broke when a constraint tightened. They compare symbolic solvers with learned components, and decide where a transformer or reinforcement learning policy helps and where it adds risk. They write evaluation harnesses, run ablations, and report results in terms the wider team can act on.

What a strong hire delivers

In the first months, a strong hire delivers a working planner on a representative problem, with a clear account of where it succeeds and where it fails. They set up an evaluation environment the team trusts. They produce feasibility and robustness results, a baseline comparison against simpler methods, and a recommendation on which approach to take into production.

How you can tell they are good

Ask them to walk through a planner they built, from representation to results. Strong candidates explain why they chose the heuristic, what they tried that failed, and how they measured constraint satisfaction and resource use. They compare against simple baselines without being asked. Be wary of anyone who reports only success rate, or cannot say what happens when the constraints change.

Why it is hard to fill

Few people sit across classical planning, learned models and operations research. Most candidates lean one way. Titles vary, so searching for planning scientist alone misses many good people. Academic planning researchers may lack production experience, while ML engineers often lack search depth. We usually advise founders to decide which side they can teach, then hire for the other.

Where to find them

Look in academic planning and search groups, operations research teams, and robotics or logistics companies where scheduling and routing are daily problems. Algorithms engineers from games, solver vendors and optimisation start-ups are strong sources. ML researchers who have worked on agent reasoning or inference-time search increasingly bring useful planning experience, and often hold titles such as research scientist or applied scientist.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Planning Algorithms Researcher

  1. Describe a planner you built. How did you choose the representation, and what would you change now?

    A strong answer names the problem, the representation options considered and the trade-offs. They admit what did not work and explain what they would change. Worry if the answer is vague, only theoretical, or credits the result to a tool.

  2. How do you judge whether a plan is good, beyond whether the task was completed?

    Look for plan success, constraint satisfaction and resource use as separate measures, plus attention to variance and failure cases. Worry if they rely on a single headline number or never inspect failed plans.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for a Planning Algorithms Researcher to develop planners that choose reliable action sequences under explicit constraints. You will work with research and engineering colleagues to turn planning ideas into tested methods that our products can depend on.

What you will do

  • Design and build planning algorithms for multi-step tasks under explicit constraints
  • Choose and refine planning representations and search heuristics
  • Build evaluation environments and test planners for feasibility and resilience
  • Compare symbolic, learned and hybrid approaches against simple baselines
  • Analyse failures and report plan success, constraint satisfaction and resource use
  • Work with engineers to bring planners into agent systems

What you will bring

  • Demonstrated work building planners or search algorithms, shown through code, papers or projects
  • Strong grounding in heuristic search and planning representations
  • Fluent Python and experience with search environments
  • Experience with symbolic solvers or optimisation methods
  • Evidence of careful, honest model evaluation
  • Ability to explain results clearly to non-specialists

Nice to have

  • Experience with reinforcement learning or transformer models
  • Background in operations research
  • Experience integrating planners into agent frameworks

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Transformer modellingAdvancedImplement attention-based sequence or multimodal models
  • Reinforcement learningAdvancedOptimise policies from interaction and reward
  • Optimisation and operations researchWorkingChoose actions subject to objectives and constraints
  • Model evaluationWorkingMeasure capability and failure modes credibly

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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