Engineer II - Robotics MLOps

Job Area:

Denied Operations Division > GNSS-Denied

Domain:

Robotic Systems

What this Role Offers

Full-Stack ML Deployment: Own the deployment of advanced robotics and ML algorithms onto high-performance edge hardware

Hardware Acceleration Ownership: Direct hands-on work with NVIDIA platforms, utilizing CUDA and TensorRT to squeeze real-time performance out of complex neural networks

Gatekeeper of Software Releases: Act as the critical final validation point in a gated release process, ensuring only the most performant, stable code reaches our UAV fleets

Impactful Technical Intersection: Operate at the nexus of MLOps, DevOps, and Robotics Engineering, where you ensure that cutting-edge R&D algorithms are optimized for mission-critical reliability

Systematic Release Governance: The authority is held to define and enforce quality gates, ensuring that only verified, performance-tuned robotics code is promoted to mission-critical deployment branches

About the Role

The software release and deployment lifecycle is architected by the Engineer II - Robotics MLOps. A critical bridge is maintained between validated R&D algorithms and field-ready deployment. This role ensures the stability of the branching strategy, manages the gated promotion of code from Staging to Development and Release, and provides the architecture for MLOps and DevOps pipelines. This position is designed for an engineer, by whom it is understood that ML in robotics is not just about the model - it is about the hardware constraints, real-time performance, and the rigour of the release process

Key Responsibilities

Release Lifecycle Management: Manage the gated promotion of software across repositories; coordinate the movement of code from Staging to Development and eventually to Release branches, aligning with flight-test verification and QA standards

ML Deployment & Optimization: Optimize machine learning models and robotics algorithms for embedded deployment by applying advanced techniques such as quantization, pruning, and knowledge distillation. Profile and tune models for hardware acceleration using CUDA, TensorRT, and other NVIDIA platform primitives

CI/CD & MLOps Infrastructure: Design and maintain the MLOps toolchain, including automated training, model versioning, and deployment pipelines that are validated against flight-test data

Release Readiness: Act as the technical lead for release candidate preparation. Coordinate with Senior Systems Engineers to ensure all logs, telemetry, and performance reports are compiled for final stakeholder approval

Observability & Diagnostics: Implement logging and health monitoring infrastructure to ensure flight-test data is structured, accessible, and actionable for the R&D team

Infrastructure & Pipeline Reliability: Maintain development environments, containers, and deployment toolchains, serving as a secondary point of contact for R&D, Embedded, and DevOps workflows

Minimum Qualifications

Bachelor's or Master’s degree in Machine Learning, Computer Science, Embedded Systems, Electronics, Electrical Engineering, or Mechatronics

3+ years (Bachelor’s) or 1+ years (Master’s) of industrial experience in ML and/or robotics software deployment

ML Production Skills: Deep proficiency in Neural Networks and hardware-specific model optimization (TensorRT, CUDA)

Software Rigour: Strong proficiency in C++ and Python with a focus on system performance, memory management, and multi-threading in Linux-based robotics environments

Testing Standards: Demonstrated experience in Test-Driven Development (TDD) and the use of automated testing frameworks (e.g., GTest, PyTest)

CI/CD Expertise: Proven experience with build automation, artefact versioning, and complex branching strategies (BitBucket/Git)

Profiling & Debugging: Experience in profiling and debugging embedded software using tools like Nsight, GDB, or Valgrind

Deployment Tooling: Experience with Docker, Kubernetes (K3S), or similar containerization tools in embedded environments

Preferred Qualifications

Domain Expertise: Familiarity with SLAM, NeuRF and Gaussian Splatting, Sensor Fusion, or Computer Vision pipelines for navigation

Safety Standards: Experience with safety-critical software development standards (e.g., MISRA C++)

Hardware Platforms: Hands-on experience with specific NVIDIA edge platforms (Jetson Orin/Xavier)

Real-Time Systems: Experience with RTOS or real-time Linux kernel customization

Evaluation Pipelines: Experience in designing and managing data-driven evaluation pipelines for robotics/ML performance metrics

Working Hours

Standard working hours are 9:30 AM to 6:30 PM, Monday to Friday

Field-testing activities may require early-morning or extended hours, depending on mission requirements

Compensation Range

Competitive compensation aligned with industry standards, including performance-based incentives

Exact salary ranges will be customised according to experience

Benefits

Comprehensive health insurance

Professional development support

Detachment allowance