Interested in this AI/ML Engineer role at hims & hers?
Apply Now →About This Role
Hims \& Hers is the leading health and wellness platform, on a mission to help the world feel great through the power of better health. We are redefining healthcare by putting the customer first and delivering access to care that is affordable, accessible, and personal, from diagnosis to treatment to delivery. No two people are the same, so we provide access to personalized care designed for results. By normalizing health \& wellness challenges and innovating on their solutions, we’re making better health outcomes easier to achieve.
Hims \& Hers is a public company, traded on the NYSE under the ticker symbol “HIMS.” To learn more about the brand and offerings, you can visit hims.com/about and hims.com/how\-it\-works . For information on the company’s outstanding benefits, culture, and its talent\-first flexible/remote work approach, see below and visit www.hims.com/careers\-professionals.
About the Role:
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Hims \& Hers is looking for an outstanding Manager, Legal Programs (Privacy \& AI) to join our growing Legal team. Splitting your time across our established privacy program and our developing AI governance program, you will partner closely with our Privacy Program Manager, Privacy and AI Counsels, and teams across the business to maintain our privacy commitments, build out responsible AI practices, and help solve complex legal and compliance problems at the intersection of healthcare, technology, and consumer products.
You Will:
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### Privacy Program
- Partner with our Privacy Program Manager to run and refine the Hims \& Hers Privacy Program in close coordination with Legal, Product, and Engineering counterparts
- Help manage compliance with various privacy laws, including GDPR, CCPA, and medical privacy laws—for example, maintaining data maps, records of processing activities, and legitimate interests assessments, and project managing various risk assessments and audits
- Conduct initial reviews on privacy risks in new initiatives, help teams mitigate those risks, and coordinate reviews requiring upper\-level review
- Support the Hims \& Hers Vendor Review Program from a privacy perspective, including developing and refining questionnaires, coordinating input from other stakeholders, and informing contract negotiations
### AI Governance Program
- Help stand up and operate the Hims \& Hers AI governance program, developing the processes, workflows, and documentation that support responsible AI use across the business
- Help build and maintain an inventory of AI systems and use cases, coordinating intake and classification as new uses arise
- Monitor the evolving AI regulatory landscape (such as the EU AI Act and emerging U.S. state AI laws) and translate new requirements into practical program updates
### Across Both Programs
- Own implementation responsibility for strategic legal initiatives that require close cross\-functional work with Product, Engineering, and Marketing
- Coordinate with Legal and Public Policy teams to monitor evolving privacy and AI legislation
You Have:
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- Required: Bachelor's Degree or equivalent work experience
- Required: 8\+ years of experience in legal project/program management with a focus on privacy and/or AI governance
- Experience managing stakeholders and cross\-functional teams from project initiation through close
- Organization skills with the experience managing multiple and sometimes competing priorities
- Project management, time\-management and organizational skills
- Demonstrated aptitude for problem\-solving and delivering operational results
- Experience operating autonomously across multiple teams
- Familiarity with AI governance frameworks or emerging AI regulation a plus
Our Benefits (there are more but here are some highlights):
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- Competitive salary \& equity compensation for full\-time roles
- Unlimited PTO, company holidays, and quarterly mental health days
- Comprehensive health benefits including medical, dental \& vision, and parental leave
- Employee Stock Purchase Program (ESPP)
- 401k benefits with employer matching contribution
- Offsite team retreats
We are committed to building a workforce that reflects diverse perspectives and prioritizes ethics, wellness, and a strong sense of belonging. If you're excited about this role, we encourage you to apply—even if you're not sure if your background or experience is a perfect match.
Hims considers all qualified applicants for employment, including applicants with arrest or conviction records, in accordance with the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance, the California Fair Chance Act, and any similar state or local fair chance laws.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Hims \& Hers is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please contact us at [email protected] and describe the needed accommodation. Your privacy is important to us, and any information you share will only be used for the legitimate purpose of considering your request for accommodation. Hims \& Hers gives consideration to all qualified applicants without regard to any protected status, including disability. Please do not send resumes to this email address.
To learn more about how we collect, use, retain, and disclose Personal Information, please visit our Global Candidate Privacy Statement.
Compensation Range: $110K \- $140K
Salary Context
This $110K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At hims & hers, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($125K) sits 43% below the category median. Disclosed range: $110K to $140K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
hims & hers AI Hiring
hims & hers has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $140K - $140K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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