Robotics Software Engineer

About Us

Mutable Tactics is building the orchestration layer for autonomous robotics: turning solo robots into cohesive teams that are smarter, faster, and cheaper to operate.

Job Description

You will own core Mastermind modules end to end and turn our decision engine (symbolic task planning, behaviour trees, probabilistic state estimation from prototypes) into deterministic, tested, real-time C++ that runs on edge compute and flies in trials.

What you'll do

• Design, implement and own core modules in modern C++: task execution, world-state management, platform adapters.

• Work with the CTO on the decision engine: PDDL-based task planning, behaviour trees, planning under uncertainty. Take components from prototype to production.

• Build the reasoning side of the world model: consume fused tracks from our perception partner, carry their uncertainty into planning, and abstract them into the symbolic state the planner acts on.

• Define and maintain the interface contract with the perception partner: message schemas, timestamping, uncertainty representation, degraded-mode behaviour.

• Set the engineering bar with the chief architect: code review, test strategy, SITL/HIL in CI, profiling on target hardware.

• Integrate partner platforms (MAVLink, ROS 2, Cursor-on-Target) and run field trials. Expect regular UK travel and occasional international trips.

• Support junior engineers through review and pairing.

Requirements

• Production experience in modern C++ (17/20): concurrency, memory ownership, performance profiling, unit/integration testing, CMake and cross-compilation. The interview includes a C++ exercise.

• Software you wrote has run on real robots or vehicles, not only in simulation.

• Hands-on experience with at least one of: symbolic/task planning, behaviour trees, MDP/POMDP decision making, reinforcement learning — and a view on when each is the wrong tool.

• Linux and ROS 2 or comparable middleware (DDS, Zenoh).

• Eligible for UK SC clearance.

Strong advantage

• Planning and decision making under uncertainty: belief states, POMDPs, probabilistic inference.

• Working knowledge of sensor fusion and multi-target tracking outputs (track states, covariances, track lifecycle) — enough to consume a fused picture and spot when it is wrong.

• ArduPilot or PX4, MAVLink, and outdoor flight testing.

• Cursor-on-Target / ATAK, STANAG 4586.

• Inference on edge hardware (Jetson, TensorRT).

• Simulation: Isaac Sim, Gazebo, Unity.

An MSc or PhD in robotics, AI or a related field is useful; equivalent industry experience counts the same.

Benefits

• Salary: very competitive market rates, plus meaningful early-stage equity.

• Location: Cambridge, hybrid; on-site 3 days a week.

• Direct ownership of core system components from day one.

• Holiday, pension, equipment budget, learning environment, top-tier team, perks, etc.