• Help turn the models built by our ML Scientists, Data Scientists, and Perception Engineers into reliable, production-grade services. • Work on the infrastructure, pipelines, and tooling that take a model or an LLM/agent-backed workflow from a research notebook to a fully monitored deployment. • Partner daily with the R&D team to understand what a model needs to run in production (compute, data inputs, versioning, post-processing). • Coordinate with the Platform and DevOps teams to provision the infrastructure, permissions, and deployment pathways. • Maintain and extend the model registry, build and debug deployment pipelines and cloud infrastructure, and set up model and pipeline monitoring and testing. • Troubleshoot issues, such as failed deployments, permissions errors, or inconsistent environments. • Help shape and document standards for how models move from staging to production. • Ensure R&D goals and challenges are well understood by Software Engineering and DevOps teams.

• Help turn models built by ML Scientists, Data Scientists, and Perception Engineers into reliable, production-grade services • Work on the infrastructure, pipelines, and tooling that take a model or an LLM/agent-backed workflow from a research notebook to a fully monitored deployment • Maintain and extend our model registry • Build and debug deployment pipelines and cloud infrastructure • Set up model and pipeline monitoring and testing • Troubleshoot issues, such as failed deployments, permissions errors, or inconsistent environments • Serve as a key communicator ensuring R&D goals and challenges are well understood by Software Engineering and DevOps teams.

Role Description As a Senior Machine Learning Engineer (Medical Imaging) , you will lead the design and deployment of end-to-end AI lifecycles for global healthcare and life sciences clients. You will move beyond building isolated models to architecting scalable, production-grade ML systems on the Databricks Lakehouse. This role requires a unique intersection of deep learning expertise, data engineering rigour, and a consultant’s mindset — balancing technical sophistication with the ability to advise stakeholders on clinical validation, regulatory compliance, and long-term AI strategy. Key Responsibilities - AI Architecture & Development: Design and implement scalable computer vision pipelines using PyTorch or TensorFlow, leveraging Databricks Runtime for ML to process large-scale medical imaging datasets (DICOM, NIfTI). - Production MLOps: Architect end-to-end MLOps workflows using MLflow for experiment tracking, model versioning, and seamless transition from research to production-grade Model Serving. - Imaging Optimization: Develop efficient data loaders and preprocessing steps for high-dimensional medical data, utilizing Apache Spark to parallelize image transformation and feature extraction. - Governance & Compliance: Enforce rigorous data lineage and security protocols via Unity Catalog, ensuring all model training and inference processes meet healthcare regulatory standards (HIPAA/GDPR/HITRUST). - Technical Leadership: Act as a strategic advisor for clients, guiding them on the "build vs. buy" of medical AI tools and sharing best practices on model interpretability and bias mitigation in clinical settings. - Standardization: Implement CI/CD for ML (Git integration, Databricks Asset Bundles) and promote the use of Feature Stores to ensure consistency between training and real-time inference. Qualifications - 5+ years in Machine Learning or Data Science, with at least 3 years of deep, hands-on experience deploying models within the Databricks ecosystem. - Proven experience handling medical imaging formats (DICOM, NIfTI, WSI) and familiarity with specialized libraries such as MONAI, SimpleITK, or OpenCV. - Advanced proficiency in Python (specifically the PyData stack) and SQL. Experience with PySpark for large-scale data manipulation is essential. - Production experience in Azure (Azure Machine Learning, ADLS Gen2) or AWS (SageMaker, S3), with a focus on GPU instance management and cost optimization. - Expertise in MLflow for the full lifecycle and experience with Delta Lake to manage unstructured imaging metadata. Familiarity with Databricks Model Serving or TorchServe. - Strong understanding of Deep Learning architectures (CNNs, Transformers, SegNet) and evaluation metrics specific to medical diagnostics. - Fluent English (C1+) for direct client collaboration; ability to translate complex algorithmic concepts into strategic business value for non-technical stakeholders. - A commitment to technical excellence, ideally backed by certifications such as Databricks Machine Learning Professional or specialized Cloud AI certifications. Requirements - Available via Employment Contract (UoP) or B2B in Poland. - Available via B2B only in Romania/Greece. - 100% Remote: Full flexibility to work from anywhere in Poland/Romania/Greece. - High-End Tech: Apple MacBook Air M4 15" provided to all engineers. - Referral Bonus: Bonus for bringing other top-tier engineers to the team. - Certification Support: Coverage for all Databricks technical certifications. - Support in reaching the highest tiers of Databricks expertise (such as the Champion program) tailored to your specific career track. - Opportunities to present at global industry conferences and contribute to technical thought leadership. - Direct collaboration with Databricks MVPs and core product teams, giving you a front-row seat to the platform's evolution. Recruitment Process - Introductory Call (20 min): Short conversation with our Recruiter to discuss your background and expectations. - Technical Interview (60 min): Deep dive into your technical skills with our engineering team. - Optional Client Interview: Required only in specific cases. - Decision & Offer: We aim to close the process and provide feedback efficiently.

• Design, build, and maintain scalable MLOps infrastructure for machine learning and Generative AI applications. • Develop automated training, validation, testing, deployment, and CI/CD pipelines for machine learning models. • Implement experiment tracking, model versioning, model registries, and artifact management using MLOps best practices. • Build and maintain workflow orchestration, feature engineering, and data processing pipelines. • Monitor production ML systems, including model performance, data quality, drift detection, latency, and overall system health. • Manage the end-to-end model lifecycle, including retraining, rollback, reproducibility, governance, and auditability. • Containerize ML workloads with Docker and deploy scalable services using cloud-native technologies and orchestration platforms. • Develop and maintain Infrastructure as Code (IaC) for AI platforms and cloud resources. • Collaborate with Data Scientists and software engineers to productionize, optimize, and scale machine learning solutions. • Evaluate and implement new MLOps tools, frameworks, and best practices, including support for LLM and agentic AI applications.