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About This Role
We Breathe Life Into Data
At Komodo Health, our mission is to reduce the global burden of disease. And we believe that smarter use of data is essential to this mission. That's why we built the Healthcare Map — the industry's largest, most complete, precise view of the U.S. healthcare system — by combining de-identified, real-world patient data with innovative algorithms and decades of clinical experience. The Healthcare Map serves as our foundation for a powerful suite of software applications, helping us answer healthcare's most complex questions for our partners. Across the healthcare ecosystem, we're helping our clients unlock critical insights to track detailed patient behaviors and treatment patterns, identify gaps in care, address unmet patient needs, and reduce the global burden of disease.
As we pursue these goals, it remains essential to us that we stay grounded in our values: be awesome, seek growth, deliver "wow," and enjoy the ride. At Komodo, you will be joining a team of ambitious, supportive Dragons with diverse backgrounds but a shared passion to deliver on our mission to reduce the burden of disease — and enjoy the journey along the way.
The Opportunity at Komodo Health
The Analytics Center of Excellence (ACE) team at Komodo Health partners with life sciences organizations to drive strategic value through data, analytics, and AI-enabled solutions. The AI Engagement Manager will play a critical role in ensuring Komodo's AI capabilities are translated into real, measurable impact for our customers.
The AI Engagement Manager exists to serve as the primary client-facing point of contact for Komodo's AI-driven initiatives. This role bridges Komodo's internal product, data science, and analytics capabilities with the strategic needs of our clients — working closely with both existing and new customers to integrate Komodo's AI offerings into their workflows, deliver measurable value to key strategic accounts, and drive renewals through sustained client impact.
Looking back on your first 12 months at Komodo Health, you will have…
- Successfully launched and delivered AI pilots with key strategic accounts, including support for major pharma partnerships.
- Established yourself as a trusted advisor to senior client stakeholders across multiple life sciences engagements.
- Built strong internal relationships across product, data science, and analytics teams to align delivery with client expectations.
- Developed repeatable frameworks and playbooks for AI engagement scoping and delivery that the broader ACE team can leverage.
- Contributed to account growth by identifying expansion opportunities within existing client relationships.
You will accomplish these outcomes through the following responsibilities…
- Own a significant portion of direct project delivery for client engagements, with support from team members as needed.
- Serve as the primary point of contact for clients throughout the AI engagement lifecycle, from initial scoping and pilot design through execution and final delivery.
- Lead strategic advisory conversations with senior client stakeholders, translating Komodo's AI capabilities into actionable solutions for their business needs.
- Manage 3-5 concurrent AI engagements, balancing timelines, deliverables, and client expectations across accounts.
- Support presales and renewal conversations by bringing credible AI and life sciences expertise to the table.
- Proactively identify risks and opportunities within client engagements, escalating or acting as appropriate.
- Contribute to the development of the AI engagement practice by documenting learnings and refining delivery processes over time.
What you bring to Komodo Health (required):
- Willingness to travel approximately 50% of the time to support client engagements.
- Experience in management consulting with a focus on strategic advisory work, preferably at a top-tier or life sciences-focused firm.
- Direct experience working with pharmaceutical, biotech, or broader life sciences clients.
- Hands-on experience scoping, managing, or delivering AI or digital transformation pilots in a client-facing context.
- Strong executive presence with the ability to lead complex conversations with senior stakeholders.
- Excellent project management skills — comfortable managing multiple engagements simultaneously in a fast-moving environment.
- Familiarity with healthcare data, commercial life sciences functions, or real-world evidence.
- Ability to operate with autonomy and adapt quickly in a high-growth, ambiguous environment.
Expectations of AI Use in this role (required):
Judgement & Creativity:
- Applies AI strategically, knowing how to use the right tools at the right time to solve problems or generate new ideas.
- Uses AI to accelerate feedback loops, test ideas, and make informed decisions faster.
- Applies both judgment and creativity when working with AI and understands its limitations.
Curiosity & Initiative:
- Demonstrates curiosity and initiative in exploring new AI tools and workflows relevant to their role.
- Has developed or shared AI-enhanced workflows, automations, or prompts with others.
Integration & Productivity:
- Brings a reflexive AI mindset and uses AI as a natural part of their process to iterate, research, and deliver better work.
- Comfortable working in AI-enhanced environments across writing, data analysis, content creation, or design.
- Regularly leverages AI tools such as Notion AI, Perplexity, Claude, Descript, Runway, Midjourney, or Microsoft Copilot i.e. to improve speed, clarity, or creative output.
#LI-Remote
Komodo's AI Standard
At Komodo, we're not just witnessing the AI revolution – we're leading it. This is a pivotal moment in time, where being first to market with AI transforms industries and sets the bar. We've already established industry leadership in leveraging AI to revolutionize healthcare, and we expect every team member to contribute. AI here isn't optional; it's foundational. We expect you to integrate AI into your daily work – from summarizing documents to automating workflows and uncovering insights. This isn't just about efficiency; it's about making every moment more meaningful, building on trust in AI, and driving our collective success.
Join us in shaping the future of healthcare intelligence.
Where You'll Work
Komodo Health has a hybrid work model with hubs in San Francisco, New York City, and Chicago. Roles vary — some can be performed from anywhere in the country, others are scoped to a specific region, and some are based near one of our hubs. For hub-based Dragons, we're building intentional in-office rhythms alongside the flexibility that's core to how we work. Whatever your setup, expectations will always be clear before you join.
Equal Opportunity Statement
Komodo Health provides equal employment opportunities to all applicants and employees. We prohibit discrimination and harassment of any type with regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
By submitting your application, you acknowledge that you have read and understand Komodo Health's Privacy Notice for Employees and Contractors.
This notice explains how we collect, use, and retain applicant data.
Salary Context
This $151K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Komodo 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $151K to $190K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Komodo Health AI Hiring
Komodo Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $190K - $190K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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