Software Dev Engineer I
About The Position
The Agentic AI and Applied Foundation Model team is looking for a Software Development Engineer to improve the reliability, maintainability, and testability of complex robotic systems. You will build tools and infrastructure that help engineers diagnose failures, automate operational workflows, reproduce software environments, and understand exactly which software, models, firmware, configurations, and assets were used in each robot test. This role sits at the intersection of robotics, developer infrastructure, automation, and machine learning systems. You will work with hardware, firmware, simulation, and applied ML teams to turn fragmented operational knowledge and manual processes into dependable software systems. The ideal candidate is comfortable working across system boundaries, investigating ambiguous failures, and delivering practical tools that make other engineers more effective.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, Robotics, or a related technical field, or equivalent professional experience.
- Software development experience using Python and C++.
- Knowledge of object-oriented programming.
- Experience designing, implementing, testing, and operating production-quality software.
- Experience with Linux systems, command-line tooling, networking fundamentals, and debugging distributed or multi-component systems.
- Experience building automation, internal developer tools, backend services, or systems integrations.
- Familiarity with APIs, structured data, databases, and event-driven or workflow-based systems.
- Ability to investigate ambiguous technical problems and turn manual processes into maintainable software solutions.
- Strong written and verbal communication skills, including the ability to collaborate across hardware and software disciplines.
Nice To Haves
- Experience with robotics, autonomous systems, embedded systems, or hardware-in-the-loop development.
- Experience with CI/CD, test infrastructure, artifact management, configuration management, or experiment-tracking systems.
- Familiarity with cloud storage and compute services, containers, and infrastructure-as-code.
- Experience with machine-learning model deployment, inference runtimes, GPU acceleration, or performance benchmarking.
- Familiarity with simulation environments, digital assets, calibration workflows, or robot fleet management.
- Experience building retrieval, search, or AI-assisted knowledge systems using documentation, runbooks, logs, and other operational data.
- Experience developing safe automation for production systems, including approval workflows, access controls, audit trails, and rollback mechanisms.
- Understanding of software and data provenance, reproducibility, versioning, and dependency management.
- Experience improving observability and responding to operational incidents.
Responsibilities
- Develop tools for diagnosing, maintaining, and upgrading robotic systems.
- Convert operational knowledge—such as troubleshooting sessions, runbooks, logs, and incident history—into structured, searchable, and context-aware support systems.
- Build automation for operational workflows, including issue triage, ownership resolution, notifications, approvals, remediation, and escalation.
- Design systems that safely automate actions on production or lab infrastructure while maintaining appropriate human review and auditability.
- Build and maintain a registry of robots, test runs, software artifacts, machine-learning models, firmware versions, calibration data, configurations, and simulation assets.
- Establish traceability between test inputs and results so experiments can be reproduced, compared, and audited.
- Identify configuration and asset drift across development, simulation, and physical systems.
- Create automated verification and regression-testing workflows for robotics software and infrastructure changes.
- Reproduce robot software stacks in development or lab environments to support debugging, migration, and system-level testing.
- Assist with separating software workloads from robot hardware and validating changes across different compute environments.
- Support the testing and performance evaluation of optimized machine-learning inference paths.
- Improve system observability through logging, metrics, dashboards, alerts, and diagnostic tooling.
- Participate in design reviews, incident response, operational support, and continuous improvement of engineering processes.
- Produce clear documentation and share technical knowledge across teams.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
