Bold is looking for a Machine Learning Engineer to build and deploy production machine learning systems, including sophisticated content recommendation models and member-specific predictions, to personalize the Bold experience and drive measurable health outcomes for Medicare members.
Requirements
- 3-5+ years developing and deploying machine learning models in production environments, with demonstrable impact on product metrics or business outcomes.
- Proven track record building recommendation systems, predictive models, or ML-powered features; experience with both supervised and unsupervised learning methods.
- Strong software engineering background with experience shipping production code, working in collaborative development environments, and maintaining ML systems at scale.
- Expert-level proficiency in Python, PyTorch, and Scikit-learn; proven ability to take models from research to production with proper testing, validation, and monitoring.
- Deep understanding of content recommendation algorithms, collaborative filtering, embeddings, and Transformer architectures for sequential and contextual predictions.
- Strong foundation in software development principles, version control (Git), CI/CD practices, and writing clean, maintainable, well-documented code that integrates seamlessly with production systems.
- Experience with Generative AI models, building agentic workflows, MLOps tools and practices (model registries, feature stores, experiment tracking), cloud platforms (AWS/GCP/Azure).
Responsibilities
- Develop and deploy production ML models that power content recommendation systems, member-specific predictions, and personalized experiences across the Bold platform, ensuring models are scalable, reliable, and clinically aligned with our mission
- Collaborate cross-functionally with data scientists, software engineers, and product managers to integrate ML capabilities into products and applications, translating business requirements into technical solutions that drive measurable member outcomes
- Build and optimize recommendation systems using supervised and unsupervised learning methods, Transformers, and state-of-the-art ML techniques to match members with the right exercise programs and interventions at the right time
- Own the full ML lifecycle from experimentation and testing to deployment, monitoring, and iteration, establishing best practices for model performance tracking, versioning, and continuous improvement
- Enable internal and external data products by creating robust ML pipelines and APIs that make predictions accessible to stakeholders while maintaining data quality, model explainability, and system reliability
Other
- The position is hybrid in Los Angeles, but we also welcome Bay Area–based candidates who can travel to LA periodically.
- You will report to the data science lead.
- Ability to translate complex technical concepts for non-technical stakeholders, work effectively in multidisciplinary teams, and balance technical rigor with pragmatic product delivery.
- Intellectually curious and action-oriented approach to staying current with ML advances, debugging complex issues, and finding creative solutions to novel problems in the healthy aging space.
- Startup or high-growth environment experience preferred–comfortable with ambiguity and wearing multiple hats.
