Staff Machine Learning Scientist

Hinge Health; San Francisco (CA); On-site; USD 150,000 - 200,000; Full time

Benefits offered by this job

Comprehensive medical, dental, and vision coverage

401k retirement plan with company match

Inclusive healthcare benefits

Job summary

Hinge Health in San Francisco is seeking a Staff ML Scientist to spearhead machine learning efforts for proactive member communications. This role will involve optimizing send-times for messages using advanced ML techniques to enhance engagement and clinical outcomes.

The ideal candidate will have extensive experience in deploying ML systems and mentoring team members across product and data science functions. Join us to make a significant impact on musculoskeletal care and improve outcomes for our members.

Qualifications

• 7+ years building and deploying ML systems in production at consumer scale.
• At least one recommendation or sequential-decisioning system shipped end-to-end.
• Fluency in experimentation and A/B testing.
• Deep understanding of machine learning and applied statistics.

Responsibilities

• Design and ship systems for sending member nudges.
• Build and deploy propensity models for member engagement.
• Set rigorous experimentation standards and lead the team.
• Mentor ML scientists and guide technical direction.

Skills

Machine Learning

Python

SQL

A/B Testing

Education

Bachelor’s degree or higher in Computer Science or related field

Tools

Databricks

Airflow

Job description

About the Role

Hinge Health helps people move without pain through digital musculoskeletal (MSK) care. That care only works when members keep doing their exercise therapy, and the right message at the right moment is a large part of what keeps them going.

As a Staff ML Scientist on the Proactive Communications & Notifications team at Hinge Health, you’ll own the machine learning that decides what message each member receives, when, and through which channel. At our scale, small gains in relevance and timing compound into large gains in engagement and clinical outcomes.

You’ll be the technical leader for ML on the team: setting direction for send-time optimization, propensity modeling, and the experimentation rigor behind every nudge we ship. You’ll write code your senior engineers respect, mentor a small ML team, and partner closely with product, data science, and our growth and marketing teams.

Our ideal candidate has shipped recommendation or sequential-decisioning systems that changed how real users behave, runs experiments with rigor, and writes code their engineers respect. They optimize for what moves for members, not model sophistication for its own sake.

What You’ll Accomplish

• Send-time and channel optimization: Design and ship the next system for deciding what nudge to send a member, when, and through which channel, beyond our current contextual-bandit approach.

• Propensity modeling: Build and deploy models that decide whether nudging a given member is worth it, balancing engagement against fatigue and unsubscribes.

• Experimentation rigor: Set the bar for how the team runs experiments: multi-arm tests, sequential testing, CUPED, and guarding against peeking, so our nudge decisions are causally sound.

• Production ownership: Own at least one model in production end-to-end.

• Leadership: Mentor the team’s ML scientists, guide technical direction, and partner across product, engineering, data science, and the growth and marketing teams.

Required Qualifications

• Bachelor’s degree or higher in Computer Science, Statistics, Operations Research, Machine Learning, or a related quantitative field

• 7+ years building and deploying ML systems in production at consumer scale

• At least one recommendation, ranking, or sequential-decisioning system shipped end-to-end (modeling, evaluation, deployment, monitoring, iteration)

• Fluency in experimentation and A/B testing: multi-arm tests, sequential testing, CUPED, and the common failure modes of online experiments

• Proficiency in Python and SQL; able to read a colleague’s PR and improve it

• Deep understanding of machine learning and applied statistics

Preferred Qualifications

• Contextual bandits or reinforcement learning operated in production

• Multi-objective optimization (engagement vs. adherence vs. retention vs. cost)

• Causal inference beyond A/B testing: difference-in-differences, synthetic controls, instrumental variables

• Cold-start and low-data-regime modeling (healthcare gets thin on per-member data fast)

• Experience hiring and growing a small ML team

• Healthcare, fintech, or other regulated-data experience; familiarity with HIPAA and BAA constraints

• Familiarity with our adjacent stack: Statsig, Databricks, feature stores, Airflow/dbt

• Familiarity with TypeScript

About Hinge Health

Hinge Health leverages software, including AI, to largely automate care for joint and muscle health, delivering an outstanding member experience, improved member outcomes, and cost reductions for its clients. The company has designed its platform to address a broad spectrum of MSK care—from acute injury, to chronic pain, to post-surgical rehabilitation—and the platform can help to ease members’ pain, improve their function, and reduce their need for surgeries, all while driving health equity by allowing members to engage in their exercise therapy sessions from anywhere. The company is headquartered in San Francisco, California.

Learn more at http://www.hingehealth.com

What You’ll Love About Us

• Inclusive healthcare and benefits: On top of comprehensive medical, dental, and vision coverage, we offer employees and their family members help with gender-affirming care, tools for family and fertility planning, and travel reimbursements if healthcare isn’t available where you live.

• Planning for the future: Start saving for the future with our traditional or Roth 401k retirement plan options which include a 2% company match.

• Modern life stipends: Manage your own learning and development

Culture & Engagement

Hinge Health is an equal opportunity employer and prohibits discrimination and harassment of any kind. We make employment decisions without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, age, veteran status, disability status, pregnancy, or any other basis protected by federal, state or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. We provide reasonable accommodations for candidates with disabilities. If you feel you need assistance or an accommodation due to a disability, let us know by reaching out to your recruiter.

By submitting your application you are acknowledging we are using your personal data as outlined in the personnel and candidate privacy policy.

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