Engineer II - Robotics MLOps

• 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

• 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

• 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

• 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

• 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

• Competitive compensation aligned with industry standards, including performance-based incentives
• Exact salary ranges will be customised according to experience

• Comprehensive health insurance
• Professional development support
• Detachment allowance

Engineer II - Robotics MLOps prepares machine-learning and robotics software for reliable deployment on embedded hardware. The role spans model optimization, release controls, automated pipelines, and validation for UAV flight testing.

Role at a glance

Skills mentioned

What you will likely work on

• Control gated movement of software across repositories and release branches, aligning promotions with flight tests and QA.
• Optimize ML models and robotics algorithms for embedded hardware, including profiling and performance tuning.
• Build and maintain automated training, model-versioning, deployment, and validation pipelines.
• Prepare release candidates and coordinate logs, telemetry, and performance reports for stakeholder review.
• Establish logging and health monitoring so flight-test data is useful to R&D teams.
• Maintain development environments, containers, and deployment tooling for R&D, embedded, and DevOps workflows.

Good fit signals

• You have industrial experience deploying ML or robotics software, with the applicable experience threshold depending on degree level.
• You bring strong C++ and Python skills and understand performance, memory management, and multithreading in Linux environments.
• You have deep neural-network and hardware-specific model optimization skills, including TensorRT or CUDA.
• You have worked with automated testing, build and release processes, and embedded software debugging.
• Familiarity with robotics navigation methods, safety-critical standards, NVIDIA edge hardware, or real-time systems is helpful but preferred.