Senior Software Engineer, Autonomy Core

Agility Robotics Agility Robotics · Robotics · Fremont +2 · Remote · Software Engineering

Senior Software Engineer to architect and develop core robot software, focusing on middleware, telemetry, and regression testing for autonomous behaviors in dynamic environments. Requires expertise in multi-process robot architectures, distributed systems, and simulation-to-real transitions.

What you'd actually do

  1. Drive the implementation of selected middleware solutions to optimize communication and scalability across the core software platform.
  2. Define, document, review, and enforce process boundaries and API contracts, driving organization-wide alignment.
  3. Collaborate closely across the robot software and architecture teams to solve complex, cross-functional problems and ensure robust performance on physical robot hardware.
  4. Own and maintain the large-scale regression pipelines to automatically measure critical autonomous behavior metrics and track key performance indicators (KPIs) across motion planning, navigation, and localization.

Skills

Required

  • Designing and implementing multi-process robot architectures
  • Distributed systems
  • Robotics middleware solutions (ROS, DDS, custom messaging)
  • Inter-process/inter-machine communication
  • Developing and maintaining telemetry/metrics pipelines (OpenTelemetry/Otel)
  • Querying and analyzing large datasets using SQL-like big data tools (Amazon Athena, Spark SQL)
  • Time-series databases (Prometheus, InfluxDB)
  • Simulation at scale (domain randomization, procedural content generation)
  • Transitioning models/behaviors from simulation to real-world physical robot hardware
  • Python
  • C++
  • CI/CD pipelines (Git, Jenkins, GitHub Actions)
  • Containerization technologies (Docker, Kubernetes)
  • Robotics fundamentals (kinematics, dynamics, controls, perception)

Nice to have

  • Cloud storage solutions and data lakes (AWS S3, Google Cloud Storage)
  • Data visualizations and dashboards (Datadog, Grafana, Apache Superset)
  • Training or deploying Reinforcement Learning (RL) agents for complex autonomous behaviors
  • Publications in top-tier robotics or related conferences (RSS, ICRA, IROS)

What the JD emphasized

  • multi-process robot architectures
  • distributed systems
  • telemetry/metrics pipelines
  • large datasets
  • simulation at scale
  • transitioning models/behaviors from simulation to real-world physical robot hardware
  • Python, C++
  • CI/CD pipelines
  • containerization technologies
  • robotics fundamentals
  • Reinforcement Learning (RL) agents

Other signals

  • robot software platform
  • telemetry and metrics infrastructure
  • regression testing
  • autonomous behavior metrics
  • key performance indicators