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About This Role
Overview:
The AI\-Native Product Lead is a team\-embedded product role responsible for translating business needs into clear, testable, and technically actionable requirements and owning the product backlog for a single AI\-Native team. This role serves as the product owner for the team, working closely with the AI\-Native Tech Lead and engineers to ensure the team is focused on building valuable, usable, and high\-quality software.
This role focuses on solution definition through requirements gathering, feature and user\-story creation, backlog prioritization, and story acceptance, while operating within a small, focused team and enabling delivery through AI\-assisted engineering workflows. A key responsibility of the AI\-Native Product Lead is to provide product intent and grounding context that guide both human and AI\-assisted development. This includes clearly articulating problem statements, constraints, desired outcomes, and acceptance boundaries so that implementation decisions
remain aligned as delivery accelerates.
The AI\-Native Product Lead also performs high\-fidelity epic validation, ensuring epics are sufficiently precise, well\-scoped, and grounded before decomposition into features and stories. This validation reduces ambiguity, prevents downstream rework, and enables effective use of AI\-assisted development.
The AI\-Native Product Lead ensures the team builds the right capabilities, with the right scope, at the right time, while maximizing delivery speed and quality. The AI\-Native Product Lead does not need to be an AI or ML specialist but must understand how AI\-enabled development changes delivery speed, iteration cycles, and product discovery.
Responsibilities:
Principal Responsibilities and Essential Duties: Product Ownership* Own the single\-team product backlog.
- Ensure continuous backlog readiness to support fast, iterative delivery.
- Make day\-to\-day scope and sequencing decisions to minimize delivery friction and rework.
Requirements \& Solution Definition* Conduct interviews and requirements workshops with business and technical stakeholders to elicit and clarify business needs.
- Translate business needs into well\-structured, testable requirements.
- Maintain clear, precise documentation that enables efficient and accurate implementation.
Product Intent \& Grounding Context* Provide clear product intent by articulating the problem being solved, desired outcomes, and constraints.
- Supply grounding context for both engineers and AI\-assisted development workflows, including domain assumptions, user behaviors, system invariants, and acceptance boundaries.
- Ensure intent and context remain current as requirements evolve, preventing misalignment.
- Act as the primary source of truth for what success looks like at the epic, feature, and story level.
High\-Fidelity Validation* Perform high\-fidelity validation of epics before decomposition into features.
- Ensure epics are sufficiently precise, well\-scoped, and grounded to support AI\-assisted implementation.
- Identify \& resolve ambiguity, hidden assumptions, and incomplete intent early to reduce downstream rework.
- Validate that epics are actionable, testable, and aligned with architectural constraints prior to execution.
- Serve as a quality gate ensuring epics are ready for accelerated delivery within an AI\-Native dev model.
Continuous Delivery Validation \& Outcome Alignment* Continuously validate work against product intent, grounding context, and expected outcomes as implementation progresses.
- Provide in\-flight clarification and refinement of requirements to maintain alignment as engineers and AI\-assisted workflows iterate rapidly.
- Perform incremental acceptance of capabilities as they emerge, focusing on correctness, completeness, and behavioral alignment rather than ceremony milestones.
- Ensure delivered functionality is usable, extensible, and aligned with domain constraints, system boundaries, and quality expectations.
- Detect gaps between intended and actual outcomes early and drive corrective action before scale or automation amplifies defects.
Stakeholder Communication* Communicate progress, scope tradeoffs, risks, and delivery outcomes to stakeholders.
- Act as the primary product point of contact for the AI\-Native team, ensuring alignment between business intent and engineering execution.
Complete all responsibilities as outlined on annual Performance Plan.
Complete all special projects and other duties as assigned.
Must be able to perform duties with or without reasonable accommodation.
Qualifications:
- Bachelor’s degree in a related field or equivalent practical experience.
- 6\+ years’ experience in product ownership, product management, or business analysis roles within Agile delivery environments.
- Strong experience translating business problems into clear, testable product and technical requirements.
- Excellent written and verbal communication skills.
- Proven analytical and problem‑solving skills, with experience in systems modeling, workflow analysis, or solution design.
- Ability to learn complex domains quickly; healthcare experience preferred.
- Experience with systems modeling, user interface design, and prototyping, decision trees, data flow.
- A wide degree of creativity and latitude is expected.
Mental Requirements:* Critical Thinking: Ability to think critically and evaluate information objectively, considering different perspectives and potential implications before drawing conclusions or making recommendations.
- Attention to Detail: must have a keen eye for detail to ensure accuracy in data analysis, interpretation, and reporting.
- Quantitative Aptitude: Strong numerical skills are essential for conducting quantitative analysis, working with statistical methods and models, and manipulating data using mathematical operations.
- Data Interpretation: skilled in interpreting data visualizations, charts, graphs, and other forms of data presentation to extract meaningful insights and communicate findings effectively.
- Communication Skills: Effective communication skills are crucial for conveying complex technical concepts and insights to non\-technical stakeholders clearly and understandably through written reports, presentations, and verbal discussions.
- Curiosity and Learning Agility: A strong desire to learn and explore new methodologies, techniques, and tools in the field of data analysis and insights generation is essential for staying current with industry trends and best practices.
- Resilience: The ability to handle pressure, adapt to changing priorities, and overcome setbacks is important in a fast\-paced and sometimes ambiguous analytical environment.
- Ethical and Integrity: Upholding ethical standards and maintaining integrity in handling sensitive data and information is paramount for building trust and credibility in the insights provided.
Physical Requirements and Working Conditions:* Remaining in a stationary position, often standing or sitting for prolonged periods.
- Repeating motions that may include the wrists, hands, and/or fingers.
- Must be able to provide a dedicated, secure work area.
- be able to provide high\-speed internet access/connectivity and office setup and maintenance.
- No adverse environmental conditions expected.
*Base compensation ranges from $90,000 to $124,000 per year. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs.* *Cotiviti offers team members a competitive benefits package to address a wide range of personal and family needs, including medical, dental, vision, disability, and life insurance coverage, 401(k) savings plans, paid family leave, 9 paid holidays per year, and 17\-27 days of Paid Time Off (PTO) per year, depending on specific level and length of service with Cotiviti. For information about our benefits package, please refer to our* *Careers page.* *This* *role is based remotely and all interviews will be conducted virtually.*
Date of posting: 03/16/2026
Applications are assessed on a rolling basis. We anticipate that the application window will close on 06/15/2026, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected.
\#LI\-REMOTE
\#LI\-RA1
Salary Context
This $90K-$124K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Cotiviti, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($107K) sits 36% below the category median. Disclosed range: $90K to $124K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Cotiviti AI Hiring
Cotiviti has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Safety, AI Software Engineer. Based in Remote, US. Compensation range: $124K - $180K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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