Machine Learning Engineer

Age Bold•Los Angeles, CA

45d•$170,000 - $200,000•Hybrid

About The Position

Bold is the leading healthy aging platform, offering personalized, evidence-based exercise programs for Medicare members that help prevent falls, reduce musculoskeletal pain, and increase physical activity levels. Innovative Medicare plans rely on Bold to deliver engaging, clinically sound exercise programs that members love to use and that achieve significant health outcomes. Bold is backed by leading investors, including Rethink Impact, Andreessen Horowitz, and Khosla Ventures. We’re looking for a hands-on, expert Machine Learning Engineer with 3-5+ years of experience who can own the end-to-end ML lifecycle. You’ll be responsible for building and deploying production machine learning systems, including sophisticated content recommendation models and member-specific predictions, to personalize the Bold experience and drive measurable health outcomes. You will leverage your expertise in Python, PyTorch, and Scikit-learn, and your strong software engineering background to establish scalable ML infrastructure and best practices. You’ll collaborate closely with data scientists, software engineers, and product managers to translate product requirements into reliable, clinically aligned ML solutions. The position is hybrid in Los Angeles, but we also welcome Bay Area–based candidates who can travel to LA periodically and reports to the data science lead. As a key member of our Data team, here’s what you’ll do:

Requirements

Bachelor's degree in Computer Science, Machine Learning, Data Science, Statistics, or related technical field (Master's or PhD strongly preferred but not required).
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.
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.

Nice To Haves

Experience with Generative AI models, building agentic workflows, MLOps tools and practices (model registries, feature stores, experiment tracking), cloud platforms (AWS/GCP/Azure).
Startup or high-growth environment experience preferred–comfortable with ambiguity and wearing multiple hats.
Health, wellness, or healthcare industry experience is a plus (especially enterprise/B2B settings).

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