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About This Role
Overview:
We are seeking a highly skilled and experienced Staff Engineer I to join our dynamic team in Payment Integrity. The Staff Software Engineer I (AI\-Native Tech Lead) is a senior individual contributor responsible for providing technical leadership, architectural direction, and engineering rigor for a team building software using AI\-native development practices.
This role focuses on how AI is used across the software development lifecycle, including design, architecture, implementation, testing, and delivery. The Staff Software Engineer I serves as a technical anchor for the team, ensuring AI\-assisted development accelerates delivery without compromising quality, security, or maintainability.
This role is not focused on machine learning or model development. Instead, it centers on building high\-quality, scalable software systems using modern AI\-assisted engineering workflows, with humans retaining accountability for design, correctness, and production readiness.
Responsibilities:
Technical Leadership \& Architecture* Lead the architecture and technical direction of new and existing applications within the team.
- Act as a key technical influencer, guiding sound engineering decisions and architectural tradeoffs.
- Design scalable systems, APIs, and services aligned with product and platform needs.
- Partner with engineering leadership on complex technical initiatives and cross\-cutting concerns.
AI\-Native Engineering Practices* Help define, refine, and apply AI\-native engineering practices across the software development lifecycle in partnership with engineering leadership.
- Guide teams in the effective and responsible use of AI\-assisted development tools for coding, testing, refactoring, and documentation.
- Review, evaluate, and refine AI\-generated code to ensure quality, security, performance, and long\-term maintainability.
- Ensure AI\-assisted development workflows are production\-ready and aligned with established engineering standards and architectural constraints.
AI Governance \& Quality Controls* Establish and enforce clear boundaries for AI agent behavior within the team, including where automation is permitted, where human review is required, and what actions are explicitly disallowed.
- Collaborate with platform and DevOps partners to ensure CI/CD quality gates (testing, security scanning, validation) are enforced and appropriately adapted for AI\-assisted development workflows.
- Provide team\-level system architecture governance, ensuring designs align with architectural principles, standards, constraints, and long\-term maintainability goals.
Quality, Standards \& Continuous Improvement* Conduct code and design reviews, providing actionable feedback and reinforcing engineering best practices.
- Identify and address technical debt, performance bottlenecks, and systemic quality issues.
- Drive continuous improvement in engineering practices, tooling, and developer workflows.
- Stay current with emerging technologies and AI\-assisted development techniques relevant to software engineering.
Mentorship \& Collaboration* Mentor Senior Software Engineers and other team members in AI\-enabled engineering practices.
- Foster a culture of technical excellence, ownership, and continuous learning.
- Collaborate closely with product leadership to align system design with product intent and delivery goals.
- Work effectively with cross\-functional partners to deliver reliable, high\-quality solutions.
This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required. This job description does not constitute an employment agreement and is subject to change as the needs of Cotiviti and requirements of the job change.
Qualifications:
Required Qualifications* Bachelor’s degree in computer science, engineering, a related field, or equivalent experience.
- 8\+ years of software engineering experience with demonstrated technical leadership.
- Strong backend or full\-stack development experience.
- Proven experience designing scalable systems and application architectures.
- Strong understanding of modern software engineering practices and design principles.
- Experience using AI\-assisted development tools in real\-world or production workflows.
- Ability to evaluate, refine, and productionize AI\-generated code.
- Proficiency in one or more of the following: Java, C\#, Python, associated frameworks, MSSQL, and Oracle PL/SQL.
- Strong analytical, problem\-solving, and communication skills.
Preferred Qualifications* Experience serving as a technical lead on delivery teams.
- Experience building or contributing to internal developer platforms, frameworks, or shared services.
- Experience improving developer productivity through tooling, standards, or workflow optimization.
- Experience with cloud platforms, microservices architectures, and DevOps practices.
- Familiarity with relational and no\-sql databases.
- Familiarity with Agile development methodologies and tools.
- Contributions to open\-source projects or engagement in the software engineering community.
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 $122,000 to $155,000 per year. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs. This role is eligible for discretionary bonus consideration.* *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.
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\#LI\-Remote
Salary Context
This $122K-$155K 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 ($138K) sits 17% below the category median. Disclosed range: $122K to $155K.
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.
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