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About This Role
How You'll Make An Impact
At Strive Health, patients come first. We're on a mission to transform chronic conditions by identifying risk earlier, coordinating thoughtful care, and supporting people through every stage of their health journey.
Our work reduces emergency visits, improves outcomes, and helps patients live fuller lives. You'll work alongside passionate Strivers who care deeply about making an impact, show up for one another as One Team, and find ways to elevate the everyday.
If you're looking for meaningful work where your contributions truly matter, you'll feel right at home at Strive!
Benefits \& Perks
- Hybrid\-Remote Flexibility –Work from home while fulfilling in\-person needs at the office, clinic, or patient home visits.
- Comprehensive Benefits – Medical, dental, and vision insurance, employee assistance programs, employer\-paid and voluntary life and disability insurance, plus health and flexible spending accounts.
- Financial \& Retirement Support – Competitive compensation with a performance\-based bonus program, 401k with employer match, and financial wellness resources.
- Time Off \& Leave – Paid holidays, vacation time, sick time, and paid birthgiving, bonding, sabbatical, and living donor leaves.
- Wellness \& Growth – Family forming services through Maven Maternity at no cost and physical wellness perks, mental health support, and an annual professional development stipend.
To learn more about our offerings, click here.
What You'll Do
The Principal AI Engineer is a senior individual contributor who helps Strive turn AI into real operating leverage across the business. This role will work as a forward\-deployed AI engineer embedded with business and functional leaders to identify high\-value problems, rapidly prototype and deploy practical AI solutions, and help teams adopt new ways of working enabled by enterprise AI platforms such as Claude Code, Glean, and related tools.
This role is not accountable for Strive's core product roadmaps. Instead, they will operate horizontally across the company, partnering with teams in clinical operations, corporate functions, care model support, and technology to automate workflows, build internal agents and copilots, improve decision support, and accelerate execution. They will combine strong hands\-on AI engineering capability with sound judgment about what is worth building, how to deploy it safely, and how to help others use AI effectively.
Reporting to the VP, Engineering, this role will also help shape Strive's broader AI enablement strategy by influencing platform and tooling recommendations, establishing repeatable patterns for safe and effective AI deployment, training technical and non\-technical users, and serving as an internal evangelist for AI transformation.
The Day to Day
- Partner directly with leaders across business units to identify, prioritize, and sequence high\-leverage AI opportunities that reduce manual work, improve speed, and increase quality.
- Act as a forward\-deployed AI engineer, embedding with teams to understand workflows in detail and translate them into practical automations, copilots, agents, and decision\-support tools.
- Design, build, and deploy internal AI\-enabled solutions using enterprise platforms such as Claude Code, Glean, retrieval\-based systems, agentic workflows, and orchestration frameworks.
- Create reusable patterns, prompts, skills, templates, runbooks, and reference implementations so successful approaches can be adopted repeatedly; help business teams replace repetitive administrative effort with AI\-assisted workflows while preserving the human judgment and relationships that matter most.
- Work closely with engineering, data, security, compliance, and clinical stakeholders to ensure AI solutions are safe, governed, maintainable, and appropriate for regulated healthcare environments.
- Influence enterprise AI tool selection, evaluation, rollout, and usage standards based on hands\-on experience and measurable business impact.
- Train and enable both technical and non\-technical users on how to use AI tools effectively, responsibly, and with the right expectations for quality, risk, and oversight.
- Communicate complex AI concepts clearly to executives, clinicians, operators, and frontline teams, translating technical tradeoffs into plain language and practical decisions.
- Measure and communicate the impact of AI deployments using operational, productivity, quality, and user adoption metrics.
- Serve as an internal evangelist for AI transformation by leading workshops, demos, and office hours; continuously improve how Strive works with AI by sharing lessons learned and recommending where to invest next.
- Meet in person with internal and/or external stakeholders to facilitate team and business priorities/opportunities. Business travel may be required for opportunities to connect with stakeholders and attend Strive\-sponsored team events.
Minimum Qualifications
- Bachelor's Degree in computer science, engineering, data science, or a related technical field, or equivalent practical experience.
- 8\+ years of experience in software engineering, machine learning engineering, data engineering, solutions engineering, or a closely related field.
- 2\+ years of hands\-on experience building and deploying AI\-powered systems, including generative AI, retrieval\-augmented systems, agentic workflows, or AI\-enabled automations.
- Demonstrated ability to operate as a senior individual contributor in ambiguous environments, independently driving work from problem framing through deployment and adoption.
- Strong software engineering fundamentals with practical experience building production\-quality tools, services, or workflows.
- Experience partnering directly with business stakeholders to translate operational pain points into technical solutions.
- Ability to communicate clearly with both technical and non\-technical audiences and to influence without formal authority.
- Internet Connectivity \- Min Speeds: 3\.8Mbps/3\.0Mbps (up/down); Latency \< 60 ms.
- Ability to travel and be onsite to meet business needs.
Preferred Qualifications
- Experience working in healthcare, value\-based care, or another regulated environment with meaningful privacy, security, and governance requirements.
- Strong hands\-on experience with enterprise AI platforms and tools such as Claude Code, Glean, coding assistants, agent frameworks, and workflow orchestration tools.
- Experience building internal AI copilots, automation tools, knowledge systems, or agentic applications that support operational or clinical teams.
- Strong Python skills and experience with cloud\-based data and application workflows, ideally in AWS.
- Practical experience with retrieval\-augmented generation, prompt and context design, evaluation methods, and guardrails for production AI systems.
- Experience operating in a forward\-deployed, solutions engineering, field engineering, or internal consulting model where speed, judgment, and stakeholder trust are critical.
- Track record of influencing tool standards, implementation patterns, and adoption practices across multiple teams.
- Experience designing and delivering training, workshops, or enablement programs for users with a wide range of technical backgrounds.
- Experience evaluating AI vendors, tools, and architectures with a pragmatic lens on value, risk, usability, and maintainability.
- Familiarity with healthcare data, clinical workflows, and collaboration with compliance, security, and legal stakeholders.
About You
- You have strong judgment about what to build, what not to build, and how to maximize practical value from AI inside a real business.
- You are highly hands\-on and enjoy turning messy operating problems into working systems that people actually use.
- You are energized by working side\-by\-side with teams, earning trust quickly, and helping others adopt better ways of working.
- You are a clear communicator who can move comfortably between engineers, operators, clinicians, and executives.
- You are excited by change and bring a bias toward action, experimentation, and continuous improvement.
- You are thoughtful about safety, governance, and risk, especially when deploying AI into sensitive workflows and data environments.
- You are team\-first, low\-ego, and motivated by enterprise impact more than ownership boundaries.
- You are passionate about helping Strive become an AI\-native company.
Annual Salary Range: $130,000 \- $196,000
Final compensation will be determined based on location, experience, and qualifications.
Strive Health is an equal opportunity employer and drug free workplace. At this time Strive Health is unable to provide work visa sponsorship. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Please apply even if you feel you do not meet all qualifications. If you require reasonable accommodation in completing this application, interviewing, completing any pre\-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to [email protected].
We do not accept unsolicited resumes from outside recruiters/placement agencies. Strive Health will not pay fees associated with resumes presented through unsolicited means.
Salary Context
This $130K-$196K 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 Strive 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 ($163K) sits 25% below the category median. Disclosed range: $130K to $196K.
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.
Strive Health AI Hiring
Strive Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $196K - $196K.
Location Context
AI roles in Denver pay a median of $201,050 across 48 tracked positions. That's 8% below 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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