What we’re doing isn’t easy, but nothing worth doing ever is. 

We envision a future powered by robots that work seamlessly with human teams. At Diligent Robotics, we build artificial intelligence that enables service robots to collaborate with people and adapt to dynamic, human-filled environments.

Diligent is one of the only companies in the world operating a production fleet of mobile manipulation robots in real environments. Every day, our robots work alongside hospital staff, generating the real-world data needed to advance the next generation of Physical AI. Building robots that operate reliably in these environments requires exceptional systems engineering across hardware and software.

As a Systems Software Engineer – Robotic Platform, you will own and guide development of the core robot platform, from firmware on peripheral devices to the Linux configuration that runs the robot’s compute stack. You’ll build robust drivers and interfaces for sensors, improve system reliability, and help the organization prototype and integrate new sensing and hardware for next-generation robots.

Your work will form the foundation on which autonomy, perception, and applications are built, ensuring our robots operate reliably at scale in real-world deployments.

Responsibilities

• Own and evolve core robot system foundations: device firmware interfaces, low-level drivers, Linux configuration, and deployment reliability
• Build and maintain robust sensor drivers and hardware interfaces with clear contracts and strong observability
• Lead efforts around sensor calibration, time synchronization, and data integrity across heterogeneous devices
• Improve reliability and reproducibility of system builds and deployments (e.g., containerization, deterministic builds, provisioning)
• Maintain system-level documentation and operational playbooks to improve debuggability and reduce time-to-fix
• Partner with autonomy/platform teams to ensure the system meets latency, throughput, and reliability requirements
• Contribute to architecture decisions that improve modularity, maintainability, and long-term scalability of the robot platform

Basic Qualifications

• Undergraduate or graduate degree in Robotics, Computer Science, Electrical Engineering, or related field.
• 5+ years of professional software engineering experience, ideally in robotics, autonomous vehicles, or other complex cyber physical systems
• Experience with sensor calibration and sensor time synchronization
• Comfort with basic networking concepts relevant to robotic systems (latency, bandwidth, protocols, debugging)
• Experience with Linux-based IoT/robot deployments, including OTA or fleet update patterns in a Linux context
• Experience with reproducible build systems and environments (Docker, Nix, or similar)
• Bonus: experience with robot software stacks, ROS, controls, or on-robot performance/debugging

What we’re doing isn’t easy, but nothing worth doing ever is. 

We envision a future powered by robots that work seamlessly with human teams. At Diligent Robotics, we build artificial intelligence that enables service robots to collaborate with people and adapt to dynamic, human-filled environments.

Diligent is one of the only companies in the world operating a production fleet of mobile manipulation robots in real environments. Every day, our robots work alongside hospital staff, generating the real-world data needed to advance the next generation of Physical AI. Debugging autonomy in the real world is fundamentally different than debugging in the lab, and solving that challenge requires exceptional tooling and infrastructure.

As a Software Engineer – Observability & Debugging, you will strengthen our team’s ability to understand, diagnose, and improve the performance of our robotics applications in production. You will work closely with robotics engineers and operations teams to build the tools, systems, and standards that allow us to debug, triage, and root-cause robot performance issues quickly and reliably.

Our goal is that every bug should be reproducible. You will help us get there by building the observability, replay, and debugging systems that make real-world robotics development scalable.

Responsibilities

• Build and maintain observability tooling that supports debugging and root-cause analysis of robot performance in real-world deployments
• Define and standardize triage workflows and instrumentation practices across the robotics stack
• Develop reliable mechanisms for collecting, curating, and replaying robot logs, events, and telemetry (“debug + replay” systems)
• Own critical incident tooling foundations, such as our structured logging and application replay systems, and evolve them into scalable, easy-to-use systems
• Improve and expand on-robot metrics generation: what we measure, how we measure it, and how quickly we can interpret it
• Integrate and extend visualization and introspection tools (e.g., Foxglove) for fast iteration and effective triage
• Partner with robotics platform and applications teams to add instrumentation to key subsystems (behavior, planning, localization, controls, etc.)
• Drive improvements in data management pipelines: upload flows, retention policies, indexing/search, and developer ergonomics
• Mentor others on best practices for instrumentation, debugging, reproducibility, and operational excellence

Basic Qualifications

• Undergraduate or graduate degree in Robotics, Computer Science, Electrical Engineering, or related field (or equivalent experience) 
• Strong proficiency in C++ and Python
• Some robotics experience (comfortable reading autonomy logs, reasoning about robot state, and debugging cross-system behaviors)
• Experience building observability/debugging systems (structured logging, metrics, tracing, event pipelines, replay tooling, dashboards)
• Familiarity with developer workflows for diagnosing distributed or real-time systems (profiling, postmortems, regression analysis)
• Nice to have:
  • Foxglove (or similar robotics visualization/telemetry tooling)
  • Log replay / bag replay systems (ROS bags or equivalent)
  • Data pipeline experience (capture → upload → storage → indexing → retrieval)

What we’re doing isn’t easy, but nothing worth doing ever is. 

We envision a future powered by robots that work seamlessly with human teams. At Diligent Robotics, we build artificial intelligence that enables service robots to collaborate with people and adapt to dynamic, human-filled environments.

Diligent is one of the only companies in the world operating a production fleet of mobile manipulation robots in real environments. Every day, our robots work alongside hospital staff, generating the real-world data needed to advance the next generation of Physical AI. If you’re excited about building autonomy that actually works outside the lab, this is one of the most interesting places you can do that work.

As a Software Engineer – Robotic Applications, you will help strengthen and extend our core robotics platform, from task management and behavior orchestration to motion planning, localization, and controls. Working closely with our Embodied AI team, you will deploy, integrate, and evaluate autonomy models on real robots operating in hospitals. This work will include one of the first production-scale deployments of Vision-Language-Action (VLA) models and AI-generated manipulation behaviors, bringing the latest advances in embodied AI into real-world operation.

Your work will directly impact how robots behave in the real world, helping us continuously improve the reliability, robustness, and usefulness of autonomy in complex human environments.

Responsibilities

• Design, implement, and maintain core robotics platform subsystems (task management, behavior systems, planning, localization, control)
• Collaborate with Embodied AI partners to deploy, evaluate, and iterate on autonomy models in real robotic applications
• Integrate emerging components (e.g., world models) into production robotic workflows with a focus on reliability and observability
• Build test harnesses, regression coverage, and debugging tools to ensure high confidence releases and fast iteration cycles
• Contribute to architecture decisions that improve modularity, testability, and long-term maintainability of the robotics stack
• Mentor teammates and raise the bar on engineering quality, reliability, and operational excellence

Basic Qualifications

• Undergraduate or graduate degree in Robotics, Computer Science, Electrical Engineering, or related field
• 5+ years of professional software engineering experience, ideally in robotics, autonomous vehicles, or other complex cyber physical systems
• Strong proficiency in C++ and Python
• Core robotics expertise in at least one major domain (e.g., planning, localization, controls, behavior systems, task execution)
• Experience shipping software on real robots and debugging issues that emerge in unstructured, real-world environments
• Strong systems thinking and comfort working across interfaces between autonomy, platform infrastructure, and operational needs
• Excellent cross functional communication skills and a bias toward pragmatic solutions

What we’re doing isn’t easy, but nothing worth doing ever is. 

We envision a future powered by robots that work seamlessly with human teams. At Diligent Robotics, we build artificial intelligence that enables service robots to collaborate with people and adapt to dynamic, human-filled environments.

Diligent is one of the only companies in the world operating a production fleet of mobile manipulation robots in real environments. Every day, our robots work alongside hospital staff, generating the real-world data needed to advance the next generation of Physical AI. If you’re excited about solving the hardest problems in robotics, where simulation meets real-world deployment, this is the job for you.

As a Senior Software Engineer – Simulation / Support, you will accelerate the effectiveness of our engineering organization. This hybrid role spans developer tooling, software support and triage, and simulation infrastructure. You will help engineers build better software, faster, while strengthening the reliability and debuggability of our robotics stack in production.

You will develop state-of-the-art infrastructure to test and validate Physical AI models, and help bridge the gap between simulation and reality by improving synthetic data and scenario generation with real-world data from our growing fleet of operational robots.

Responsibilities

• Design and build tools that improve productivity and debugging across the robotics software stack
• Develop and maintain simulation infrastructure used for testing autonomy, navigation, perception, manipulation and system level behaviors
• Partner with autonomy, perception, platform, and infrastructure teams to integrate simulation into CI pipelines and validation workflows
• Create reproducible test harnesses and simulation scenarios that mirror real world operational edge cases found in our customer environments
• Support release readiness by strengthening automated validation, regression testing, and scenario based simulation coverage
• Contribute to architecture decisions that improve modularity, testability, and long term maintainability of the codebase
• Evaluate and integrate third party simulation frameworks or build custom simulation tooling as needed
• Mentor engineers on best practices in testing, debugging, observability, and system design

Basic Qualifications

• Undergraduate or graduate degree in Robotics, Computer Science, Electrical Engineering, or related field.
• 5+ years of professional software engineering experience, ideally in robotics, autonomous vehicles, or other complex cyber physical systems
• Strong proficiency in C++ and/or Python
• Experience with robotic middleware such as ROS or similar distributed systems frameworks
• Experience developing or extending simulation environments, such as Gazebo, Isaac Sim, Mujoco, or custom in house engines
• Experience debugging distributed systems and working with real world hardware
• Strong understanding of software architecture, systems design, and scalable tooling
• Familiarity with CI/CD systems and automated testing frameworks
• Comfort working across the stack, from low level systems to higher level autonomy behaviors
• Excellent cross functional communication skills and a bias toward pragmatic solution

What we’re doing isn’t easy, but nothing worth doing ever is. 

We envision a future powered by robots that work seamlessly with human teams. We build artificial intelligence that enables service robots to collaborate with people and adapt to dynamic human environments. Join our mission-driven team as we build out current and future generations of robots.

As an ML Engineer II (Navigation), you will develop learning-based navigation models that enable Moxi to move naturally and safely around people, beds, wheelchairs, and equipment. You’ll train policies using fleet data (imitation learning) and refine behavior with simulation and RL. Your work will directly impact delivery speed, reduced hesitation/deadlocks, and fewer interventions in real hospital deployments.

Responsibilities

• Develop learning-based navigation models that predict safe, smooth trajectories from sensor inputs and/or perception representations.
• Build imitation learning pipelines from fleet logs (trajectory extraction, filtering, scenario balancing, evaluation).
• Implement simulation-based refinement (RL, reward shaping, domain randomization) to improve robustness.
• Define navigation success metrics aligned to product outcomes.
• Collaborate with the AI Platform team to integrate learned policies behavior/safety systems and validate on-robot.
• Build regression tests and scenario replay suites for challenging scenarios.
• Analyze field behavior, identify failure modes, and close the loop through data curation and retraining.

Basic Qualifications

• Bachelor’s or Master’s degree in Robotics, Computer Science, Electrical Engineering, or related field (PhD a plus).
• 3+ years of experience in ML for robotics, autonomy, or sequential decision-making.
• Strong proficiency in PyTorch and experience with sequence models / policy learning.
• Experience with imitation learning and/or reinforcement learning in robotics or autonomy contexts.

Preferred Qualifications

• Experience with socially-aware navigation, dynamic obstacle avoidance.
• Experience with RL at scale (simulation rollouts, distributed training, stability/debugging).
• Familiarity with ROS navigation stacks and safety constraints for mobile robots.
• Experience building eval harnesses (offline replay, scenario libraries).
• Experience with Vision-Language-Action (VLA) models, behavior cloning, and/or transformer/diffusion policies for robotic control.

What we’re doing isn’t easy, but nothing worth doing ever is. 

We envision a future powered by robots that work seamlessly with human teams. We build artificial intelligence that enables service robots to collaborate with people and adapt to dynamic human environments. Join our mission-driven team as we build out current and future generations of robots.

As an ML Engineer II (Manipulation), you will develop and deploy learning-based manipulation systems that enable mobile robots to interact reliably with the physical world in dynamic human environments. You’ll build perception-to-action models, training datasets, evaluation tooling, and deployment pipelines that improve robustness, generalization, and safety for real-world manipulation tasks at scale. Your work will directly impact the robot’s ability to perform complex interactions consistently across real sites with minimal special-case engineering.

Responsibilities

• Develop learning-based manipulation models for end to end sensor-driven interaction (e.g., reaching, motion generation, and execution in dynamic environments).
• Build and maintain manipulation training pipelines: dataset creation from robot logs/teleop, action representations, augmentation, and distributed training.
• Design evaluation metrics and regression tests that quantify manipulation reliability, recovery behavior, and safety in real environments.
• Develop sim-to-real workflows for manipulation learning, including simulation environments, domain randomization, and failure-mode testing.
• Optimize and distill models for edge deployment; benchmark latency, memory use, and stability on target hardware.
• Partner with the AI platform team to integrate policies with control and safety systems, and validate end-to-end performance on robots.
• Analyze field performance, identify dominant failure modes, and drive iterative improvements through data collection and targeted retraining.

Basic Qualifications

• Bachelor’s or Master’s degree in Robotics, Computer Science, Electrical Engineering, or related field (PhD a plus).
• 3+ years of experience applying ML to robotics manipulation, visuomotor control, or sequential to sequence models.
• Strong proficiency in PyTorch and experience building reliable training/evaluation pipelines.
• Strong software engineering skills in Python; ability to collaborate across ML and robotics teams.

Preferred Qualifications

• Experience with Vision-Language-Action (VLA) models, behavior cloning, and/or transformer/diffusion policies for robotic control.
• Experience with sim-to-real training for manipulation (Isaac Sim/Mujoco or similar), including domain randomization and synthetic data.
• Experience deploying ML models to edge hardware (ONNX/TensorRT, quantization, performance profiling).
• Familiarity with safety-critical robotics integration and designing fallback/recovery behaviors.