Senior Software Engineer - Physical AI

As a senior software engineer in Physical AI, you are the primary architect of the systems that transform massive, raw sensor data into high-utility, training-ready datasets for frontier ML models for LiDAR and Computer Vision applications. You will play a central role in building the end-to-end ML pipelines that automate object-detection, labelling, rich-embedding indexing, and curation at an unprecedented scale.
This is a high-impact position where you will solve the complex engineering challenges inherent in distributed compute and autonomous data flywheels. You directly deliver business impact by building the automated QA and closed-loop benchmarking infrastructure that ensures the integrity and safety of frontier models in physical environments. As a technical leader, you will drive engineering excellence and influence positive outcomes in ambitious, large-scale projects.
Responsibilities:
Build, train, and deploy high-accuracy ML models for object detection, tracking, and SLAM, while pioneering auto-labelling architectures to solve for the long-tail of rare edge cases in Physical AI.
Design and deploy high-availability ML pipelines featuring closed-loop observability, enabling models to learn constantly from real-world telemetry and extensibility by design.
Lead the engineering of complex solutions that label, index, and curate Uber-scale datasets using deep meta-context and semantic embeddings across distributed compute infrastructure.
Design and implement sophisticated, automated QA checks and evaluation frameworks to benchmark the performance of frontier models against mission-critical KPIs.
Serve as an escalation point for incident management and provide high-quality feedback during code and design reviews to maintain elite engineering standards.
Identify and advocate for improved performance, efficiency, and reduced technical debt in software, systems, and processes across teams.
Partner with a broad range of stakeholders to solve architectural gaps and consistently deliver on organisational goals through all lifecycle stages of engineering projects.
Requirements:
Extensive hands-on experience building, training, and deploying advanced ML models for Physical AI applications (LiDAR, Computer Vision) in domains such as Robotics, Autonomous Systems, or safety monitoring.
Demonstrated ability to design and deploy high-availability, high-performance "constant learning" ML models, ensuring observability and extensibility are foundational to the system design.
Proven experience working with very large-scale datasets and highly advanced, complex, distributed compute infrastructure.
Solid foundations in computer science, including data structures, algorithms, and system design tailored for large-scale data processing.
Proven track record of successfully delivering multiple cross-team projects from inception to production readiness, taking direct responsibility for end-to-end results.
Proficiency in multithreaded programming and memory management, with a strong capability for analysing logs and debugging complex, high-concurrency software systems.
Ability to identify and solve architectural gaps in ML tooling and best practices to reduce technical debt and improve company-wide engineering standards.
Preferred Qualifications:
Hands-on experience working with foundational Physical AI models and supervised fine-tuning (SFT) of advanced, large-scale models
Direct experience building end-to-end systems for Autonomous Vehicles (AVs), industrial robotics, or high-complexity computer vision applications involving sensor fusion (LiDAR, Camera, Radar, IMU).
A track record of publishing in peer-reviewed journals or conferences, reflecting a deep engagement with the research community.
Proven ability to develop and deploy complete, production-ready solutions independently, from initial research through to real-world edge deployment.

Experience
6-10 yrs