Interested in this AI/ML Engineer role at Verana Health?
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About This Role
*Verana Health is a digital health pioneer transforming raw clinical data into medical breakthroughs. As the exclusive data and analytics partner for premier medical societies, we manage a secure, curated network of over 90 million longitudinal health records \- trusted by top global pharmaceutical companies and thousands of clinicians alike. By bridging clinical reality with advanced AI, we accelerate clinical trials, unlock real\-world evidence, and directly improve patient outcomes. We are a collaborative team of problem\-solvers deploying compliant, production\-grade solutions that solve healthcare's hardest data challenges, and we want you to help us shape what's next.*
\*This hybrid role is based in San Francisco and requires commuting to our South Financial District office up to three times per week. Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time.\*
### Senior Product Manager, Agentic Solutions
Your Impact
As the Senior Product Manager of Agentic Solutions, you'll play a pivotal role in shaping our next chapter of AI innovation. By architecting autonomous AI agents and multi\-agent workflows, you will help transform how healthcare providers optimize care delivery and how the life sciences industry leverages data to develop next\-generation therapies.
Translating advanced agentic AI solutions into practical healthcare workflows, this role is a catalyst to reducing provider workflow burdens, streamlining life sciences pipelines, and elevating the quality and speed of patient care.
Your Core Focus
- Own the product vision, strategy, and execution for AI agents and multi\-agent workflows tailored for both life sciences (e.g., clinical trial matching, protocol design, safety reporting) and provider\-facing environments (e.g., clinical decision support, chart abstraction).
- Standardize how clinicians and researchers interact with autonomous agents. Define the guardrails, confidence thresholds, and user interfaces that ensure safe, transparent, and collaborative AI decision\-making.
- Partner closely with engineers, data scientists, and prompt engineers to move agents from proof\-of\-concept/notebooks to scalable, reliable production software.
- Collaborate with clinical, legal, and security teams to ensure agents operate ethically and strictly adhere to regulatory compliance standards.
- Spend time with end\-users to deeply understand their pain points and translate them into technical AI requirements (e.g., tool\-use, RAG architectures, API integrations).
- Establish product metrics to evaluate agent accuracy, task completion rates, cost\-per\-token efficiency, and user trust over time.
Your Blueprint for Success
- 5\+ years of product management experience, with a proven track record of shipping production\-grade AI/ML products; specific experience building with LLMs and GenAI is preferred
- Bachelor's degree in business, technical, or life sciences disciplines
- Deep conceptual understanding of foundational models, Retrieval\-Augmented Generation (RAG), prompt engineering, and API\-driven architectures
- Exceptional communication skills with a demonstrated ability to distill highly technical AI concepts into compelling, business\-oriented narratives for non\-technical executives, clients, and clinicians
- Ability to travel as needed (up to 25%) to partner sites and industry conferences to represent the Verana Health brand and differentiated capabilities
- Any of these experiences would add value but are not essential for success:
- + Advanced degree in Computer Science, Data Science, or a clinical field
+ Strong familiarity with the healthcare ecosystem, clinical data workflows, or life sciences/biopharma drug development pipelines
+ Familiarity with health data standards and ontologies (e.g., OMOP, FHIR, ICD\-10, SNOMED)
+ Experience in an early\-stage startup or an innovation/incubator team within an established digital health company
+ Experience operating as a solo or foundational Product Manager on an ambiguous, fast\-moving initiative
Why Join Us?
Verana Health transforms drug lifecycle and medical practice insights using our exclusive real\-world data network. We recently secured $150 million in Series E funding, backed by industry leaders like Johnson \& Johnson Innovation – JJDC, Inc., Novo Growth, GV, Casdin Capital, and Merck Global Health Innovation Fund.
We offer comprehensive benefits that include:
- Market\-leading medical, dental, and vision insurance plans
- 401(k) match
- Flexible time off programs
- Paid parental leave
- Annual learning and wellness stipend
### Salary Range: $151,400 \- $189,250 (A geographic premium may be applied to base salary)
Final note: You don't need to match every listed expectation to apply for this position. At Verana, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
*Caution to Job Applicants: Be vigilant against potential scams. Verana Health will never ask for payment or personal information upfront. Verify company details, cross\-check job offers, and trust your instincts. Any legitimate job offer will be received by a Verana Health email account (not via gmail, text or other means) Report suspicious activities to protect yourself and others in the job\-seeking community.*
Salary Context
This $151K-$189K range is below the median 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 Verana Health, 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 Required
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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($170K) sits 22% below the category median. Disclosed range: $151K to $189K.
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.
Verana Health AI Hiring
Verana Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $189K - $189K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national median.
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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