**N-iX**is looking for a **Senior Machine Learning Engineer** to join a high-impact initiative in the life sciences domain. You will be responsible for designing, developing, and deploying machine learning models at scale within the Palantir Foundry ecosystem, enabling data-driven decision-making across R&D;, commercial, and real-world evidence use cases. You will collaborate closely with data scientists, MLOps engineers, and data engineers to build robust, production-grade ML workflows—from data preparation and feature engineering to model training, evaluation, deployment, and monitoring. **Key Responsibilities** - Design and implement scalable ML models for use in predictive analytics, forecasting, and classification tasks within the pharmaceutical domain. - Work with Palantir Foundry to build end-to-end ML pipelines, including custom Python code, Foundry Functions, and Ontology-aware feature generation. - Collaborate with Data Engineers to ensure high-quality, model-ready data flows from ingestion to inference. - Operationalize models using industry best practices for versioning, reproducibility, and monitoring (e.g., via MLflow or native Foundry tools). - Contribute to MLOps automation, including CI/CD for ML, drift detection, retraining pipelines, and evaluation dashboards. - Partner with business stakeholders and domain experts to translate scientific or commercial hypotheses into model-based solutions. - Stay informed on the latest developments in ML/AI and proactively introduce innovative techniques and frameworks. **Must-Have Skills & Experience** - 5+ years of experience in machine learning or applied data science, ideally in a production or enterprise setting. - Strong programming skills in Python, with deep experience in machine learning libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch. - Experience designing and deploying ML workflows at scale, preferably with experience in Foundry, KubeFlow, SageMaker, or similar platforms. - Familiarity with feature engineering, data imputation, sampling strategies, and evaluation techniques - Hands-on experience with model deployment and monitoring, including logging metrics, detecting drift, and managing model lifecycles. - Comfort working with structured and unstructured data: tabular, time series, text, etc. - Solid understanding of data security, privacy, and compliance, particularly in pharma or regulated domains. - Strong communication and stakeholder engagement skills; capable of explaining complex models in simple terms. - Upper-Intermediate or Advanced level of English is required. **We offer**: - Flexible working format - remote, office-based or flexible - A competitive salary and good compensation package - Personalized career growth - Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more) - Active tech communities with regular knowledge sharing - Education reimbursement - Memorable anniversary presents - Corporate events and team buildings - Other location-specific benefits - not applicable for freelancers