Machine Learning Engineer

Job Description In this role, you will formulate, train and deploy machine learning models for robotic systems that perceive the world through vision and sensors, learn structured representation in data, and operate reliably in real‑world industrial environments. You’ll translate research into clean, production‑grade software that improves system performance and accelerates deployment. The focus is reducing unnecessary complexity with scalable and reliable designs. You’ll push beyond incremental improvements by applying ideas from foundation models and generative AI, leveraging synthetic data generation, and validating results on real robots. Your work will raise the bar for capability, reliability, and speed of iteration. About Us VIKASO LTD is a UK-based robotics company redefining how industrial robots are deployed, controlled, and scaled. We are building next-generation hardware and software platforms that abstract complexity, standardise integration, and enable rapid adoption of robotics across manufacturing and logistics environments. Our approach centres on robot-agnostic technologies that transform robotics from fragmented systems into a scalable, productised platform. While enabling automation at scale, our focus remains on advancing robotics - making robots easier to deploy, more intelligent to operate, and more reliable in production. At the intersection of software, control systems, and mechatronics, we are building a globally scalable robotics platform that will define the future of industrial automation. Job requirements PhD in ML/Robotics/Computer Vision or 3+ years of applied ML experienceStrong background in deep learning (computer vision), state-estimation, and core ML mathematics (probability theory, statistics, optimisation, etc).Proficiency in Python (required) and C++ (preferred for robotics integration)Experience with ML frameworks such as PyTorch (preferred), TensorFlow, or JAXProven track record building production ML systems, not just research prototypesStrong software engineering fundamentals: modular design, testing, and version controlExperience with cloud/GPU training infrastructure and MLOps workflowsAbility to design evaluation and benchmarking frameworksHands-on experience with pose estimation, object detection/segmentation, camera calibration, and sensor integration Job responsibilities Key objective is to develop and deploy robust deep neural networks for robotics, including object detection, segmentation, and scene understanding and 3D reconstruction.Build scalable pipelines for training, fine‑tuning, inference, and real-time optimization for reliability and performanceDevelop and maintain data pipelines for collection, ingestion, curation, versioning, and synthetic data generationWork with distributed training systems (multi‑GPU/cloud) and integrate models into robotics pipelines.