Senior Machine Learning Engineer- Applied Modeling

## About the Role We are hiring a Senior ML Engineer to advance machine learning on the Empower Healthcare Platform. Note on Role Type: This is an Applied Modeling position, rather than an MLOps or Infrastructure role. While you will own the ML lifecycle, your day-to-day focus will be heavily indexed on core machine learning methodology: feature and target design, data exploration, training strong supervised baselines, and rigorous evaluation on real-world tabular data. The emphasis for this role is new modeling: experimentation, feature and target design, training strong baselines and improved models, and rigorous evaluation. You will partner with backend engineers for integration, but the primary expectation is depth in ML methodology and healthcare-relevant signals, not primarily platform or MLOps ownership. We are focused on applying machine learning to solve meaningful, real-world problems in a clinical healthcare industry. Our team partners closely with product, engineering, and domain experts to translate data into actionable insights and automated solutions. Machine Learning Engineers own the full lifecycle - from data exploration and model development to deployment, monitoring, and iteration in production. ## Responsibilities - Partner with backend engineers for integration. - Focus heavily on core machine learning methodology: feature and target design, data exploration, training strong supervised baselines, and rigorous evaluation on real-world tabular data. - Drive new modeling: experimentation, feature and target design, training strong baselines and improved models, and rigorous evaluation. - Own the full lifecycle - from data exploration and model development to deployment, monitoring, and iteration in production. - Translate data into actionable insights and automated solutions by partnering closely with product, engineering, and domain experts. - Solve messy clinical data problems through feature engineering and model accuracy. - Collaborate on inference APIs, batch jobs, and failure modes. - Read and contribute to a shared codebase (FastAPI services, configuration, tests). - Follow security and privacy guidance from the broader team regarding PHI and clinical workflows. ## Requirements - **Applied Modeling Focus**: Deep interest in the data math and methodology over infrastructure. You are excited to roll up your sleeves to solve messy clinical data problems through feature engineering and model accuracy, partnering with our backend team who assists with the deployment tooling. - **Core ML**: Strong Python and hands-on experience training, evaluating, and shipping supervised models (classification and related tasks). Comfortable with scikit-learn-style pipelines, feature preparation, cross-validation, and model diagnostics (calibration, drift concepts, error analysis). - **Relevant experience**: A track record of applied ML in industry or equivalent (e.g. multiple shipped or production-adjacent projects). A PhD is not required; demonstrated impact and judgment matter more than degree level. - **Data**: Experience with tabular data at scale. Ability to write or collaborate on SQL and to reason about features from a data warehouse (we use Snowflake heavily, including Snowpark and ML-related integrations). Healthcare or regulated-domain experience is a strong plus. - **Production awareness**: Enough familiarity to collaborate on inference APIs, batch jobs, and failure modes. You do not need to own the entire MLOps stack; clarity on what "good enough to hand off" looks like is important. - **Software engineering**: Can read and contribute to a shared codebase (FastAPI services, configuration, tests). Comfortable with Git, code review, and documenting training assumptions and reproducibility. - **Compliance awareness**: Understanding that PHI and clinical workflows impose constraints on logging, data retention, and access. Willingness to follow security and privacy guidance from the broader team. ## Nice to Have - **Cloud ML**: Azure Machine Learning or equivalent (SageMaker, Vertex). - **Snowflake**: Snowpark Python, warehouse-side processing, or SQL tuning for feature extraction. - **Orchestration**: Temporal or similar for durable batch workflows. - **LLMs and NLP in production**: Ensemble or multi-model setups, clinical NLP, or entity extraction patterns. - **MLOps**: Experiment tracking, automated retraining, or evaluation in CI (supporting, not the main focus of the role). ## About the Company Empower is a high-growth, innovation-driven company focused on delivering impactful solutions that transform how healthcare organizations operate. Our team combines deep industry expertise with a commitment to excellence, enabling us to solve complex challenges at scale. We foster a collaborative, results-oriented culture where high performers thrive, ideas are valued, and continuous improvement is expected. We move quickly, think strategically, and hold a high bar for performance, while supporting each other in achieving ambitious goals. ## Healthcare Domain - DRG, ICD-10, appeals, utilization review, or EHR-derived features. - *Preference will be given to candidates that demonstrate domain knowledge and experience working on clincial workflow related models.