# Staff Machine Learning Scientist

**Company:** [Hinge Health](https://hotfix.jobs/companies/hinge-health)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $205k – $307k/yr
**Experience:** 7+ years
**Skills:** Python, SQL, Machine Learning, Contextual Bandits, Reinforcement Learning, A/B Testing, Cuped, Causal Inference, Recommendation Systems, Multi-Objective Optimization, Statsig, Databricks, Airflow, dbt, TypeScript
**Posted:** 2026-06-12

> Own ML systems for send-time optimization, propensity modeling, and nudge decisions at consumer scale. Set experimentation standards and mentor a small ML team.

## Job Description

## 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

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**Apply:** https://hotfix.jobs/jobs/73ca9fb4-e9c2-431d-89fe-f62c0b49b311
**Canonical:** https://hotfix.jobs/jobs/73ca9fb4-e9c2-431d-89fe-f62c0b49b311