MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- Lead development of machine learning models for fraud detection. Collaborate with cross-functional teams to enhance data-driven decision-making.
Responsibilities
- You will lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data
- You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed.
- You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
- You productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness.
- You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve.
- Identify and implement foundational improvements to how the team builds models.
- You will collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.
Requirements
- 6+ years experience researching, training, tuning and launching ML models at scale. Relevant PhD can count for up to 2 years of experience.
- Track record of delivering high impact machine learning models in a low latency live setting
- Strong Python skills and experience writing production-quality code.
- Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar).
- Experience with a deep learning framework (PyTorch preferred).
- Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar).
- Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).
- Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.
- You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code.
- You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.
- Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.
- You have strong verbal and written communication skills that support effective collaboration with our global engineering team.
Benefits
- Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents
- Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses
- Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge
- ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount
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