Senior Machine Learning Engineer

About Us Machnet Medical Robotics, founded in 2020, is on a mission to revolutionise medical robotics. Our guiding principle is simple: innovation must improve patient outcomes, support clinicians without disrupting workflows, and empower healthcare staff rather than adding burden.

MMR is a well-funded company with long-term investors and a strong financial foundation. Backed by an exceptional hardware and software team, the company has already built a robust medical robotic platform and achieved important technical and operational milestones.

With this solid base, MMR is now entering an exciting new chapter: integrating advanced artificial intelligence to redefine how robotics can transform healthcare. This work sits within a highly regulated medical-device environment, where safety, reliability, traceability, and real-world clinical value are essential.

You will be a core member of the AI team, developing AI systems that operate in real clinical environments. This role focuses on delivering production-grade machine learning systems that enhance clinical outcomes, provide decision support, and unlock new capabilities in medical robotics.

You will work across the full ML lifecycle, from problem definition and data strategy through to deployment, monitoring, and continuous improvement. You will be expected not only to build models, but to define robust ML architectures, establish engineering best practices, and ensure that deployed systems are reliable, reproducible, and fit for use in a regulated medical-device setting.

This is a hands-on senior role for someone who can bridge research and engineering, work effectively across disciplines, and take ownership of ML systems deployed in real-world healthcare environments. The role requires close collaboration with software, robotics, embedded, clinical, regulatory, and quality teams.

• Translate clinical and product requirements into ML problems, datasets, metrics, and evaluation plans.
• Define the data pipeline, from raw and uncurated data to model-ready and traceable datasets.
• Develop robust training, validation, and evaluation pipelines with strong reproducibility and traceability.
• Analyse model failures and drive targeted improvements in data, model design, and system performance.
• Deploy ML models into cloud, edge, or real-time environments in collaboration with software, robotics, and embedded teams.
• Establish monitoring for model performance, drift, reliability, and post-deployment safety signals.
• Contribute to verification, validation, risk management, and technical documentation required for regulated medical-device development.
• Communicate technical decisions, trade-offs, and risks clearly across teams.

Degree in Computer Science, Machine Learning, Engineering, Mathematics, Physics, Robotics, or a related STEM field, or equivalent practical experience.

Five or more years of proven experience building, deploying, and maintaining ML systems in production, beyond research prototypes.

Strong Python skills and hands-on experience with modern ML frameworks such as PyTorch or TensorFlow and MLOps frameworks such as ClearML, Flyte, and MLFlow.

Demonstrated experience across the full ML lifecycle: data preparation, training, evaluation, deployment, monitoring, and iterative improvement.

Strong software engineering practices, including testing, maintainability, code quality, and collaborative development workflows.

Experience working in a regulated or safety-critical environment, ideally in medical devices, healthcare.

MSc or PhD in a relevant technical field.

Experience in medical devices, medtech, healthcare AI, or robotics.

Experience developing ML systems under design controls or within a Quality Management System.

Demonstrated experience with Kubernetes and deploying solutions on any cloud provider, such as AWS, GCP, and Azure.

Experience with computer vision, multimodal models, time-series or procedural clinical data.

Experience designing and implementing ML pipelines in line with medical-device regulatory expectations (e.g. FDA and EU MDR), including traceability, validation evidence, and change-control for ML components.

Exposure to C++, Linux environments, and embedded platforms such as NVIDIA Jetson, IGX, or similar systems.

Track record of taking ML systems from concept to reliable production deployment in real-world settings.

The opportunity to help shape AI at the forefront of medical robotics, with direct impact on patient care and clinical practice.

A fast-growing, well-funded company with ambitious long-term plans and a strong technical foundation.

Close collaboration with clinicians, engineers, and regulatory specialists working on real products used in real clinical contexts.

A competitive compensation package benchmarked to attract outstanding talent in MedTech and AI.