layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Lead Engineer, Robot Behavior and Decision-Making at Andromeda Robotics | LAYIQ

Design the system. Challenge our initial concepts as you discover what a full shift actually needs: is it formulated as an agent harness ingesting scene graphs and acting over robust, typed action primitives? Or will you invoke World Models and VLA components? You will shape the Mission Autonomy roadmap for Andromeda.
Prototype the system. Get a mission autonomy loop running in simulation early. Use it to decide what we build, before the company commits teams to an architecture.
Co-design and build simulation capabilities with our simulation engineer.
Own the technical direction and run the design conversations with autonomy, chatbot, perception, audio and platform teams. Turn them into contracts those teams can build against.
Shape the action primitives. Work out what the mission needs from the layers underneath. Specify it properly. Get it built, whether that means convincing the owning team or writing it yourself.
Design for degradation. Define how Abi should behave when audio, perception, connectivity, navigation, or platform health degrades. Make those moments safe, useful, and legible to operators, while preserving Abi’s character rather than treating every failure as a generic error state.
Establish deployment patterns. Create a repeatable path from simulation prototypes to demonstrations on the robot, with the configuration, observability, test gates, and rollback mechanisms needed to iterate safely.
Build the evaluation system. Define benchmarking protocols, evaluation methods, and reporting that let us continuously measure the longest-horizon missions Abi can complete, the interventions they require, and the failure modes that limit them.
Your first 90 days
Month 1: Learn about Abi in the real world: read the Concept of Operations document, challenge its assumptions, and spend time with Abi in an aged-care home. Bring the autonomy, conversational AI, perception, audio, platform, and simulation teams together to define how the system makes decisions and how the layers work together. Deliver the first architecture and a clear six-month build plan, including what not to build.
Month 2: Build a working slice in simulation: Abi drives to a resident’s room, arrives safely, and starts a conversation. Use simulation to discover what breaks when the resident is absent, the route changes, or a subsystem degrades. Turn those lessons into better interfaces, a repeatable evaluation harness, and the next set of hypotheses and assumptions to test.
Month 3: Take the prototype from simulation to Abi in the lab. Establish the deployment, monitoring, replay, and evaluation patterns that show how far Abi can run autonomously, what stops her, and why.

Skills and Experience Required
5+ years of experience building autonomous systems, with an MS or PhD in Robotics, Computer Science, or a related field, or equivalent practical experience.
Previous autonomy experience in areas such as self-driving, mobile robotics, warehouse robotics, field robotics, or similar, including at least one deployment lifecycle from concept through deployment and operations.
Experience with ROS 2, Nav2, and simulation tools such as Isaac Sim, Gazebo, or MuJoCo.
Awareness of safety-critical design principles for robots operating around people.
Strong software engineering skills in Python, C++, or Rust. Comfortable working across the stack, from high-level planning and behavior to low-level robot interfaces and runtime constraints.
Practical robotics experience. Comfortable bringing up hardware, debugging sensors and actuators, and tinkering with the robot in the office.
Strong communication and influence skills, particularly when working across Australian teams and with engineers whose systems you do not directly own.
Architecture and software depth. Able to steer the system design, define interfaces and contracts, and write the first versions of the primitives rather than wait for another team.
Bonus points
Experience building mission autonomy systems in robotics, defense technology, or similar domains.
Research experience, ideally at master’s or PhD level, applying LLM techniques to long-duration robotic task planning, orchestration, tool use, or error recovery.
Experience designing simulation-based evaluation systems, including benchmarks, scenario harnesses, metrics, failure taxonomies, and reporting for long-horizon missions.
Experience with robot planning and behavior architectures, including Planning Domain Definition Language (PDDL), task-and-motion planning, behavior trees, or finite-state machines for baselining.
Technical judgment. Able to reason about trade-offs explicitly, and earn the trust of senior autonomy engineers and the teams that own adjacent parts of the stack.

Lead Engineer, Robot Behavior and Decision-Making

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