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About This Role
OUR MISSION
We exist to create a more connected, compassionate, and confident experience for people with cancer and those who care for them. We make it easier to get answers, access high\-quality care quickly, and feel supported throughout treatment and beyond.
Today, Thyme Care is a market\-leading value\-based oncology care enabler, partnering with national and regional health plans, providers, and employers to deliver better outcomes and lower costs for thousands of people across the country. Our model combines high\-touch human support with powerful technology and AI to bring together everyone involved in a person’s cancer journey: caregivers, oncologists, health plans, and employers.
As a tech\-native organization, we believe technology should strengthen the human connection at the center of care. Through data science, automation, and AI, we simplify complexity, improve collaboration, and help care teams focus on what matters most: supporting people through cancer.
Looking ahead, our vision is bold: to become a household name in cancer care, where every person diagnosed asks for Thyme Care by name. If you’re inspired to make cancer care more human and to help reimagine what’s possible, we’d love to meet you. Together, we can build a future where every person with cancer feels truly cared for, in every moment that matters.
WHAT YOU’LL DO
We're hiring an AI Program \& Governance Lead to make sure our AI Program at Thyme Care stays safe and trusted while drastically expanding the scope of use cases and value delivered to our members, stakeholders and the organization. You'll own the program and the operating system that ensures we manage AI risk by ensuring teams have the appropriate tooling, infrastructure, guidance, training, enablement and controls without slowing builders down.
This isn't a compliance checkbox role. You need real technical depth (software engineering or security engineering background), and you’ll need to stay at the frontier of what's possible with AI and translate that into practical controls and enablement by making real infrastructure available that improves the product, engineering and cross\-functional dev environments and corporate environments to take advantage of AI capabilities and ship value in production products and workflows. You'll work daily with Product, Eng, Data, Clinical, Ops, Security, and Legal, and you’ll be a key stakeholder on our AI Governance Committee.
Additionally, you will:
- Own AI governance intake: ensure pilots, purchases, and launches go through proper security and risk reviews before they happen
- Facilitate governance committee reviews and drive decisions to resolution
- Build and maintain the operational infrastructure of our AI program: documentation, audit trails, escalation pathways
- Operationalize evals, labeling and monitoring in ways that serve PMs, engineers, clinicians and other cross\-functional stakeholders
- Own training and enablement: help non\-technical teams understand AI capabilities, governance and potential opportunities for value
- Coordinate with Security/Privacy on vendor risk, BAAs, and compliance requirements
- Translate AI controls into practical, operationalized team workflows that don't create unnecessary friction
### What Great Looks Like
- Governance decisions happen quickly and with clear rationale, and teams know exactly what's required before launching an AI feature and don't find it burdensome
- We have clean audit trails that would satisfy external scrutiny, and effective controls that keep members safe and our brand trusted among stakeholders
- We have infrastructure that serves various teams’ unique needs to make adopting or using AI capabilities an easy choice
- You're seen as an enabler, not a blocker
WHAT YOU’VE DONE
- You’re deeply engaged with the AI ecosystem, and stay current on recent AI tooling/innovation
- Technical foundation: ideally, a software engineering or security engineering background
- Program management experience in regulated, high\-trust environments (security, privacy, compliance)
- Excellent cross\-functional facilitation; can drive decisions across stakeholders with different priorities
- Operational rigor: documentation, artifacts, audit trails, escalation pathways, process improvement are second nature
- Can translate AI controls into practical team workflows
Nice\-to\-haves:
- HIPAA or healthcare security/privacy experience
- Experience implementing governance for AI or ML/LLM systems (evals, monitoring, release gates)
- Vendor risk and procurement coordination experience
- Comfort owning enablement/training programs for non\-technical audiences
OUR VALUES
*At Thyme Care, our core values guide us in everything we do: Act with our members in mind, Move with purpose, and Seek diverse perspectives. They anchor our business decisions, including how we grow, the products we make, and the paths we choose—or don’t choose.*
*Our salary ranges are based on paying competitively for our size and industry, and are one part of the total compensation package that also includes equity, benefits, and other opportunities at Thyme Care. Individual pay decisions are based on several factors, including qualifications, experience level, skillset, and balancing internal equity relative to other Thyme Care employees. The base salary for this role is $178,500\-$240,000\. The salary range could be lower or higher than this if the role is hired at another level. This position is also bonus\-eligible.*
*We recognize a history of inequality in healthcare. We’re here to challenge the status quo and create a culture of inclusion through the care we give and the company we build. We embrace and celebrate a diversity of perspectives in reflection of our members and the members we serve. We are an equal\-opportunity employer.*
*Be cautious of* *recruitment fraud**, and always confirm that communications are coming from an official Thyme Care email.*
Salary Context
This $178K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At thyme care, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($209K) sits 15% above the category median. Disclosed range: $178K to $240K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
thyme care AI Hiring
thyme care has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US. Compensation range: $185K - $240K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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