Responsibilities include:
- Work closely with our machine learning and data science teams to understand their requirements and contribute to the development of scalable ML model frameworks.
- Pair with data scientists to troubleshoot model performance issues in terms of both latency and prediction quality.
- Prototype and demo new machine learning tooling functionality for data scientists
- Design, implement, and maintain infrastructure for ML model testing and deployment.
- Leverage cloud platforms and containerization technologies to ensure scalability and flexibility.
- Implement automation processes for seamless integration of ML models into the development pipeline.
- Maintain and extend monitoring solutions to track the performance of ML models in real-time.
- Ensure compliance with industry standards and regulations related to data science and ML.
- Lead the model infrastructure components and prioritization on our product roadmap
Requirements
- 2+ Years of Machine Learning Engineering or MLOps experience
- Bachelor's or Master's degree in Computer Science, Mathematics, or related field.
- Proven proficiency in utilizing MLOps tools (SageMaker, MLflow, etc.) to deploy, monitor, and manage models in production environments.
- Strong proficiency in Python and SQL, with plenty of familiarity using popular libraries for machine learning (e.g. scikit-learn, XGBoost, LightGBM, PyTorch) and data manipulation (e.g. Pandas, NumPy, Polars, DuckDB, Dask).
- Experience applying software engineering best practices to both greenfield and brownfield development (e.g. testing, CI/CD, containerization, observability)
- Excellent technical communication and collaboration skills, with a passion for being at the helm of challenging problems in a fast-paced environment.
The Ideal Fit
- Deep passion for applying cutting-edge machine learning and AI technologies to improve healthcare outcomes and patient experiences.
- A self-starter who thrives in ambiguous, fast-paced environments and takes ownership of solving complex problems end-to-end.
- Prior experience working with healthcare data, predictive models, or clinical workflows is a strong plus.
- Comfortable working in cloud-based environments, with familiarity in AWS services and modern ML tooling.
- Hands-on experience with Infrastructure-as-Code (Terraform), automation, and scripting languages such as Python and Bash.
- Strong skills in data visualization and dashboarding tools (Apache Superset, Grafana) for communicating insights effectively.
- Understanding of CI/CD/CT pipelines, version control (Git), and MLOps best practices.
- ·Familiarity with workflow orchestration frameworks like Airflow or Prefect for managing data and ML pipelines.
- Experience in Natural Language Processing, leveraging Python libraries such as NLTK, spaCy, or Hugging Face Transformers.
- Exposure to the Go programming language for performance-oriented components.
- Knowledge of containerization technologies like Docker for building, packaging, and deploying scalable ML applications.
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What We Do
Algorex is a data-science- and analytics- as-a-service company. We augment core operational systems (EHR, care management, CRM) with data outputs and intelligence generated by our platform. The Algorex platform is a combination of pre-licensed data sets covering 97% of the households of the US, purpose-built algorithms, and a set of integrations (to insert output into your operational systems). Our clients use this our platform to increase/maximize revenue (reduce member churn, align plan-product enrollment) and manage medical costs (identify rising sources of risk, introduce social determinants into the medical management process) in support of value-based care results.









