Founding Engineer, Robot Behavior and Decision-Making

Andromeda-Robotics; San Francisco (CA); On-site; USD 200,000 - 240,000; Full time

Job summary

Andromeda is building the social intelligence layer for humanoid robots. We seek a Mission Autonomy engineer to found the MA team, architect Abi’s long-duration decision-making, and coordinate with voice, perception, and platform groups.

You will drive architecture, prototyping, and evaluation across simulation and real-world experiments. You will balance research with practical robotics, shaping interfaces, contracts, and deployment patterns to enable Abi to operate autonomously over multi-hour

Qualifications

• 5+ years building autonomous systems with an MS/PhD or equivalent practical experience.
• Deployment lifecycle experience from concept through deployment and operations in robotics.
• Proficiency with ROS 2, Nav2, and simulation tools (Isaac Sim, Gazebo, MuJoCo).

Responsibilities

• Design the Mission Autonomy system architecture for Abi and shape its roadmap.
• Prototype the mission autonomy loop in simulation early to guide architecture decisions.
• Co-design and build simulation capabilities with the simulation engineer.
• Own the technical direction across autonomy, chatbot, perception, audio and platform teams.
• Define and refine action primitives, interfaces, and contracts across teams.
• Design for degradation to keep Abi safe, useful and with her character.
• Establish repeatable deployment patterns from simulation to demonstrations on Abi.
• Build an evaluation system with benchmarks and metrics for long-horizon missions.

Skills

Autonomous systems

Robotics

Python

C++

ROS 2

Nav2

Simulation tools

Communication

Education

MS or PhD in Robotics/CS

Tools

Isaac Sim

Gazebo

MuJoCo

Nav2

Job description

The Bigger Picture

At Andromeda, we're building the social intelligence layer for humanoid robots - technology that allows robots to understand people, build relationships and become a meaningful part of everyday life.

Our first robot, Abi, is already doing this in aged care and healthcare, where she brings conversation, companionship and engagement to people every day.

Now we're scaling from early deployments into a platform that can operate across thousands of real-world environments. We're looking for individuals who want to tackle hard problems across robotics and AI, and help define what it means for robots to live and interact alongside people.

The Role

We are looking for an engineer to found our Mission Autonomy team, which will own our robot’s long-duration decision-making layer and orchestrate the totality of Abi’s day. Mission Autonomy continuously decides what Abi should do next, balances task-level trade-offs, and adapts the robot’s plan across an eight-hour shift. It sits above and orchestrates Abi’s existing layers that perform conversations, waypoint navigation and obstacle avoidance, perception, gestures, and operator tooling.

As the founding Mission Autonomy team member, you will propose and build Andromeda’s Mission Autonomy (MA) architecture from the ground up, using frontier models and evaluation (both sim and real-world) to train and launch MA, and work with our voice agent, mobile autonomy, and gestures teams to design harnesses through which MA can execute behaviors on Abi. Once built, MA should guide Abi through a home, choose when to join an interaction, keep her character, and recover when the plan breaks. There is no playbook for this. You’ll need a keen eye for model evaluation and trade-offs, balancing architectural leadership with strategic hands‑on work. The work combines research with practical robotics.

Key Responsibilities

• 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.

The expected base salary for this role is $200,000 to $240,000 USD. Actual compensation will be determined by a variety of factors, including the candidates’ job related knowledge, skills and experience.

If you’re excited about this role but don’t meet every requirement, that’s okay; we are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Our Values

• Empathy – Kindness and compassion are at the heart of everything we do.
• Play – Play sharpens focus. It keeps us curious, fast and obsessed with the craft.
• Never Settle – A relentless ambition, bias toward action, and uncomfortable levels of curiosity.
• Tenacity – Tenacious under pressure, we assume chaos and stay in motion to adapt and progress.
• Unity – Different minds. Shared mission. No passengers.

Let’s build the future together.

Please note: At this time, we are generally not offering visa sponsorship for this role.