Senior Machine Learning Engineer

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.
• Apply rigorous experimentation and statistical validation to ensure models are explainable, generalizable, and regulatory-compliant.
• 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.