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About This Role
At Claritev, our mission is to simplify healthcare workflows, improve transparency, and bend the healthcare cost curve . We believe that data, technology, and AI can fundamentally transform how healthcare operates by automating complex workflows, improving decision\-making, and reducing unnecessary costs across the system.
By combining deep healthcare expertise with advanced analytics and AI, we help payers, providers, and employers operate more efficiently and deliver better outcomes for the people they serve.
We are bold in our thinking, rigorous in execution, and committed to service excellence for every stakeholder. Our culture values innovation, accountability, diversity of thought, and collaboration.
Join us as we accelerate our transformation into a leading technology and AI\-driven company shaping the future of healthcare.
JOB SUMMARY:
We are seeking a Principal Applied AI Scientist to lead the research, development, and deployment of advanced machine learning and AI systems that power Claritev’s next generation of healthcare products.
This is a hands\-on technical leadership role for an experienced applied scientist who thrives at the intersection of research innovation, real\-world deployment, and measurable business impact . You will architect and deliver predictive, generative, and agentic AI systems that automate complex healthcare workflows and unlock new insights from large\-scale healthcare data.
In this role, you will work closely with Product, Engineering, and business leaders to translate cutting\-edge research into scalable production solutions that improve transparency, reduce costs, and simplify healthcare operations.
You will also serve as a technical thought leader and mentor , helping shape Claritev’s AI strategy and elevating the scientific rigor and innovation of the organization.
KEY RESPONSIBILITIES:
AI \& Machine Learning Leadership
- Lead the research, design, and deployment of machine learning and AI systems in production environments.
- Architect predictive, generative, and optimization models that drive healthcare cost reduction, transparency, and operational efficiency.
- Develop advanced analytics, forecasting, and decision\-support models that improve customer and client outcomes.
Innovation \& Applied Research
- Research and evaluate emerging AI technologies, foundation models, and agentic systems to identify opportunities for new products and capabilities.
- Design and implement novel machine learning and optimization methodologies tailored to complex healthcare data and workflows.
- Drive experimentation and rapid prototyping to accelerate innovation and product development.
Product \& Business Impact
- Partner with Product, Engineering, and business stakeholders to translate AI capabilities into scalable solutions.
- Identify opportunities to leverage Claritev’s data assets to create new AI\-driven products, insights, and automation capabilities .
- Collaborate directly with clients to gather feedback and ensure solutions deliver measurable value.
Engineering \& Production Excellence
- Write high\-quality, scalable, and production\-ready code .
- Work closely with Engineering and DevOps teams to deploy and maintain models in production systems.
- Develop monitoring frameworks to ensure model performance, reliability, and data quality over time.
Collaboration \& Mentorship
- Serve as a technical mentor and leader for applied scientists and data scientists.
- Foster a culture of scientific rigor, experimentation, and knowledge sharing .
- Collaborate across disciplines including product management, engineering, analytics, and operations.
Governance \& Compliance
- Ensure compliance with HIPAA and healthcare data privacy regulations .
- Implement responsible AI practices including model validation, monitoring, and risk management .
Requirements: JOB REQUIREMENTS:
Basic Qualifications
Ph.D. or M.S. in Computer Science, Statistics, Applied Mathematics, Data Science, or a related field
6\+ years of hands\-on industry experience in applied ML / AI, with a track record of delivering solutions from prototype to production
2\+ years of experience with Generative AI and/or agentic systems
Preferred Qualifications
Experience in healthcare or other regulated environments
Familiarity with ML Ops / LLM Ops practices
Experience integrating predictive ML with generative/agentic systems
Proficiency with AI coding tools (e.g., Copilot, Codex)
Technical Expertise
Strong proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
Experience with:
Agentic frameworks (e.g., LangChain, LangGraph, Autogen, crewAI)
RAG pipelines, embeddings, and retrieval systems
Agent design (tool use, planning, memory, orchestration)
Experience building evaluation pipelines for ML/LLM systems (accuracy, reliability, latency)
Strong foundation in ML theory, deep learning, statistics, and optimization
Experience with cloud environments and large\-scale data systems
Leadership \& Impact
Strong product mindset, aligning technical work with business outcomes
Ability to drive projects end\-to\-end with minimal oversight
Excellent communication and collaboration skills
Proven ability to influence cross\-functional teams and mentor others
COMPENSATION
The salary range for this position is $190K to $210K. Specific compensation offers are determined based on a variety of factors including the candidate’s education, experience, skills, work location, and internal equity considerations In addition to base salary, this position is eligible for an annual performance bonus and a comprehensive benefits package, including health insurance and a 401(k) retirement plan.
BENEFITS
We realize that our employees are instrumental to our success, and we reward them accordingly with very competitive compensation and benefits packages, an incentive bonus program, as well as recognition and awards programs. Our work environment is friendly and supportive, and we offer flexible schedules whenever possible, as well as a wide range of live and web\-based professional development and educational programs to prepare you for advancement opportunities.
Your benefits will include:
Medical, dental and vision coverage with low deductible \& copay
Life insurance
Short and long\-term disability
Paid Parental Leave
401(k) \+ match
Employee Stock Purchase Plan
Generous Paid Time Off – accrued based on years of service
WA Candidates: the accrual rate is 4\.61 hours every other week for the first two years of tenure before increasing with additional years of service
10 paid company holidays
Tuition reimbursement
Flexible Spending Account
Employee Assistance Program
Sick time benefits – for eligible employees, one hour of sick time for every 30 hours worked, up to a maximum accrual of 40 hours per calendar year, unless the laws of the state in which the employee is located provide for more generous sick time benefits
EEO STATEMENT
Claritev is an Equal Opportunity Employer and complies with all applicable laws and regulations. Qualified applicants will receive consideration for employment without regard to age, race, color, religion, gender, sexual orientation, gender identity, national origin, disability or protected veteran status. If you would like more information on your EEO rights under the law, .
APPLICATION DEADLINE
We will generally accept applications for at least 5 calendar days from the posting date or as long as the job remains posted.
\#LI\-MZ1
Salary Context
This $190K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Claritev, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($200K) sits 12% above the category median. Disclosed range: $190K to $210K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Claritev AI Hiring
Claritev has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $145K - $210K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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