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About This Role
Description:
Are you looking for an emerging, AI-augmented hybrid role that combines the strategic vision of a product manager, the design sense of a UX designer, and the prototyping capability of a software engineer? We have that opportunity available in our AI Product Builder role.
We’re looking for our next bold leader – an AI Product Builder – to join our quest to provide market-transforming solutions for businesses, care teams and consumers to interactively manage health and care. This is a remote, US-based role reporting to the SVP of Product.
At Medecision, you’ll have the chance to work with people that have incredible intellect, who are hard-wired for problem-solving. You’ll also know that you’re making a real difference, and not just in some abstract, corporate-mission-statement kind of way. We’re working with companies that touch millions of lives every day, maybe even saving a few along the way. Each person contributes uniquely to our mission and our ability to raise the level of experience we provide to all our customers and colleagues. The role of AI Product Builder is no different, and in fact, plays a key role in shaping strategy and roadmap for our products, and in our journey to innovate solutions that solve real problems for our clients.
To be successful at Medecision the AI Product Builder will understand what a customer centric organization entails and be able to infuse that ideology in your contributions. On a given day, you will help drive Medecision’s success by working closely with stakeholders and the development team to define and oversee products from start to finish, translating customer and market needs into capabilities that delight our customers and improve outcomes for the members and patients they serve. Unlike traditional product managers who only write requirements and hand them off to engineering queues, you will directly create, test, and ship working features or minimum viable products (MVPs) using modern AI coding assistants, low-code platforms, and rapid prototyping tools such as Claude and Cursor. You will collaborate with a variety of business partners, including engineering, analytics, and customer success teams, and juggle multiple tasks in a dynamic, fast-paced environment.
Requirements: What You’ll Do
- AI-Enabled Product Ownership: Own product deliverables across the SDLC, from discovery through rollout. Use Claude (or equivalent) for product artifacts and drive collaboration across engineering, operations, and go-to-market teams
- End-to-End Problem Discovery: Conduct user interviews, analyze customer feedback, and identify key user pain points to determine what actually needs to be built
- Hands-on Prototyping: Build functional mockups, scripts, or early-stage features independently by leveraging AI-assisted tools (such as Claude, Cursor, or similar generative AI coding environments)
- Rapid Shipping and Iteration: Move ideas quickly from concept to a live environment where real users can interact, test, and provide feedback
- Data-Driven Optimization: Analyze usage metrics and user behavior post-launch to continuously refine, fix, or expand the product functionality
- Complete other duties as assigned
- Cross-Functional Collaboration: Act as the bridge between technical execution and business strategy, collaborating with founders, engineers, and designers without getting bogged down by traditional organizational handoff delays
- Suggest and lead process improvements that increase product delivery speed and quality
- Follow and comply with all security policies and procedures established by the organization, including adherence to HIPAA and HITRUST regulations
- Applicants must be authorized to work for any employer in the US. This position does not offer sponsorship for employment visas.
What You’ll Bring
- Bachelor’s degree in Computer Science, Business, Healthcare Management, related field, or equivalent work experience required
- Minimum of 5 years’ experience as a Product Manager, Product Owner, or Business Analyst, with exposure working in healthcare IT or a SaaS environment; prior experience as a developer or business analyst is a plus
- Strong understanding of healthcare industry concepts and terminology (awareness of Health Plan – provider markets)
- Demonstrated experience with Claude or a similar AI tool
- Comfortable speaking with clients about their goals and objectives and translating those into concrete deliverables
- Experience analyzing customer and product data sets, including performance, benchmarks, and reporting requirements
- Experience with business intelligence dashboarding tools such as QuickSight, Tableau, Power BI, or Jaspersoft is a plus
- Experience working in Agile development environments, partnering with engineering, UX, and QA
- Hybrid Skillset: Demonstrated capability or strong foundational background spanning product management (roadmapping, metrics), UX, UI design (prototyping, wireframing) and coding
- Technical Fluency: Comfort with reading, generating, or modifying code, alongside a strong familiarity with AI development tools and version control (like GitHub)
- Product Mindset: A deep bias toward action and shipping, focusing on solving core human problems rather than just building speculative features
- Scrappiness & Curiosity: Ability to independently navigate ambiguity and rapidly learn new domains or technical stacks
- Communication Skills: Clear, concise writing and verbal skills to articulate product rationale, document learnings, and share metrics with stakeholders
- Ability to communicate thoughts, concepts, and practices effectively at all levels, adjusting as needed to a target audience
- Possesses an innovative spirit and is high energy, with the ability to multi-task, prioritize, and adapt quickly in a rapidly changing environment
- Strong organization, documentation, change management, and project management skills
- Background Diversity: Often filled by former software engineers looking for product ownership, or product managers who have learned to code using AI
Benefits at Medecision
As we aim to reimagine healthcare for everyone, we also care for each of our employee's overall wellbeing. Along with your typical health and retirement benefits, some other components of our total rewards package include:
- Remote-first working environment that allows for a flexible schedule
- New Hire Allowance to get your home office up & running plus a monthly Remote Stipend to cover on-going costs like your home internet bill
- Paid Leave of Absence options including Parental Leave for welcoming a new child, Caregiver Leave for taking care of a loved one who is ill and other programs are available
Are you ready? We are! Join us in our journey to innovate solutions that solve real problems and partner with our clients to transform healthcare.
This description has been designed to indicate the general nature and level of work performed by an employee within this position. The actual duties, responsibilities, and qualifications may vary based on assignment. Medecision is an Equal Opportunity Employer (EOE) and strongly supports diversity in the workplace.
Salary Context
This $145K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At MEDecision, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($160K) sits 25% below the category median. Disclosed range: $145K to $175K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
MEDecision AI Hiring
MEDecision has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in PA, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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