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About This Role
Your Future Evolves Here
Evolent partners with health plans and providers to achieve better outcomes for people with most complex and costly health conditions. Working across specialties and primary care, we seek to connect the pieces of fragmented health care system and ensure people get the same level of care and compassion we would want for our loved ones.
Evolent employees enjoy work/life balance, the flexibility to suit their work to their lives, and autonomy they need to get things done. We believe that people do their best work when they're supported to live their best lives, and when they feel welcome to bring their whole selves to work. That's one reason why diversity and inclusion are core to our business.
Join Evolent for the mission. Stay for the culture.
What You’ll Be Doing:
Job Title: Sr Product Manager (Agentic User Interface)
Evolent is building a new AI platform from the ground up, and this role designs how people will actually work with it. As Sr Product Manager for the agentic user interface, you will own the experience through which clinicians, nurses, and operations teams collaborate with AI agents every day: what an agent surfaces, when it asks for a human decision, how it explains itself, and how a reviewer stays in control while practicing at the top of their license. The architecture is still being shaped, the first capabilities are moving into production now, and the company is backing the platform as a central piece of its strategy with dedicated product, engineering, applied AI, and data science teams and standing visibility to the executive team. This is a role for someone who is passionate about user interfaces, working alongside clinicians to design them, and genuinely curious about how the nature of work changes when AI becomes a teammate rather than a tool. If you want to define what that looks like in healthcare, and see it change how care actually gets delivered, this is that role.
Responsibilities
- Lead the product lifecycle for the agentic user interface from strategic planning to tactical activities, including ideation, discovery, development, launch, and post-launch analysis.
- Define and articulate the product vision, strategy, and roadmap for how users interact with AI agents, ensuring alignment with the overall AI platform roadmap, Evolent's company goals, and market trends.
- Conduct thorough user research, competitive analysis, and customer feedback gathering (clinicians, operations teams, and clients) to identify unmet needs and opportunities.
- Translate complex business requirements and user needs into detailed product specifications, user stories, and acceptance criteria.
- Collaborate closely with UX design, engineering, architecture, applied AI, data science, and other cross-functional teams to ensure successful product development and delivery.
- Prioritize the product backlog and manage feature development, ensuring optimal resource allocation and timely delivery against platform and capability delivery dates.
- Develop and execute go-to-market and adoption strategies, including positioning, messaging, training, and enablement materials for the internal teams and clients who use the interface.
- Monitor product performance, analyze key metrics such as adoption, task completion, and user trust in agent output, and identify areas for improvement and iteration.
- Act as a product evangelist, communicating product value and benefits to internal and external stakeholders.
- Mentor and provide guidance to junior product managers and team members.
- Lead AI product strategy and roadmap development for the GenAI-enabled capabilities surfaced through the agentic user interface.
- Partner with data science and engineering leaders to define scalable AI architectures, model evaluation frameworks, and production readiness criteria.
- Own definition, execution, and measurement of proof-of-concepts and pilot initiatives to validate emerging technologies and interaction patterns.
- Define product requirements for AI-powered features, including model quality expectations, human-in-the-loop and governance requirements, and user adoption measures.
- Drive decisions regarding AI feature prioritization based on customer value, operational efficiency, and technical feasibility.
Required Qualifications
- Bachelor's degree in a relevant field (e.g., Computer Science, Data Science, Engineering, Business, Healthcare Administration) or equivalent practical experience.
- Minimum 5-7+ years of experience in product management, with a strong emphasis on software products in the healthcare technology sector, including 2+ years owning user-facing applications or platform products consumed by other teams.
- Proven track record of successfully launching and managing complex software products.
- Demonstrated ability to define and execute product strategy, roadmap, and go-to-market plans.
- Strong grounding in user experience (UX) principles and design thinking, with experience partnering with designers and researchers to ship interfaces that people adopt and trust.
- Hands-on product experience with generative AI systems in production, such as LLM-based workflows, retrieval and document processing pipelines, evaluation and monitoring, and human-in-the-loop review patterns.
- Working knowledge of modern AI technologies and the trade-offs among them: model selection, prompting and orchestration approaches, retrieval-augmented generation, fine-tuning versus prompting, and agentic patterns.
- Proven ability to partner with architects and engineers on technical trade-offs, including build versus buy versus partner, canonical data models, and API and event contracts.
- Experience delivering software in a regulated environment with sensitive or protected data (PHI/PII), including audit, traceability, and access-control requirements.
- Strong analytical skills with the ability to interpret data, identify trends, and make data-driven decisions.
- Excellent communication, presentation, and interpersonal skills, with the ability to effectively communicate with technical and non-technical stakeholders.
- Experience with Agile development methodologies (e.g., Scrum, Kanban) and proficiency in product management tools (e.g., JIRA, Confluence, Aha!).
- Ability to thrive in a fast-paced, dynamic environment and manage multiple priorities effectively.
Preferred Qualifications
- Master's degree in a relevant field (e.g., Business, Computer Science, Data Science, Human-Computer Interaction, Healthcare Administration, Engineering).
- Deep understanding of the healthcare industry, including payer, provider, and value-based care models; experience in prior authorization, utilization management, appeals, or payer and provider clinical operations is a plus.
- Direct experience leading GenAI or machine learning product initiatives from concept through launch, and bringing AI-enabled products to enterprise scale.
- Experience shipping conversational, copilot, or agent-driven interfaces, including patterns for explainability, confidence and citation display, escalation to a human, and reviewer override.
- Working knowledge of healthcare interoperability standards such as FHIR, X12 278, and HL7, and of CMS Interoperability and Prior Authorization rules.
- Depth in Microsoft Azure as a primary platform (e.g., AKS, Service Bus, Azure SQL and Cosmos DB, Azure AI Foundry, Azure OpenAI, Document Intelligence, AI Search). Comparable AWS or Google Cloud depth considered.
- Experience with agent architectures and orchestration frameworks, embedding and retrieval pipelines, and model routing through a centralized AI gateway.
- Knowledge of MLOps and LLMOps practices, including evaluation datasets and scoring, shadow-mode validation, drift and quality monitoring, and observability tooling such as OpenTelemetry.
- Exposure to identity, access, and lifecycle governance for AI systems and agents, and to HITRUST or SOC 2 certification programs.
- Experience with product analytics and experimentation tools, including A/B testing and usability research methods.
- Experience collaborating with data scientists on experimentation, model tuning, and deployment strategies.
To ensure a secure hiring process we have implemented several identity verification steps, including submission of a government issued photo ID. We conduct identity verification during interviews, and final interviews may require onsite attendance. All candidates must complete a comprehensive background check, in-person I-9 verification, and may be subject to drug screening prior to employment. The use of artificial intelligence tools during interviews is prohibited and monitored. Misrepresentation will result in immediate disqualification from consideration.
Technical Requirements:
We require that all employees have the following technical capability at their home: High speed internet over 10 Mbps and, specifically for all call center employees, the ability to plug in directly to the home internet router.
Evolent is an equal opportunity employer and considers all qualified applicants equally without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability status. If you need reasonable accommodation to access the information provided on this website, please contact [email protected] for further assistance.
The expected base salary/wage range for this position is $120,000 - 135,000. As part of our total compensation package, Evolent is proud to offer comprehensive benefits (including health insurance benefits) to qualifying employees. All compensation determinations are based on the skills and experience required for the position and commensurate with experience of selected individuals, which may vary above and below the stated amounts.
Salary Context
This $120K-$135K range is in the lower quartile 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 Evolent, 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($127K) sits 40% below the category median. Disclosed range: $120K to $135K.
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.
Evolent AI Hiring
Evolent has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in Remote, US. Compensation range: $135K - $135K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 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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