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About This Role
Company Overview:
Acentra Health exists to empower better health outcomes through technology, services, and clinical expertise. Our mission is to innovate health solutions that deliver maximum value and impact.
Lead the Way is our rallying cry at Acentra Health. Think of it as an open invitation to embrace the mission of the company; to actively engage in problem\-solving; and to take ownership of your work every day. Acentra Health offers you unparalleled opportunities. In fact, you have all you need to take charge of your career and accelerate better outcomes – making this a great time to join our team of passionate individuals dedicated to being a vital partner for health solutions in the public sector.
Job Summary and Responsibilities:
Acentra Health is looking for a Machine Learning Engineer/AI Engineer, Associate to join our growing team. Job Summary:
The purpose of this position is to support the design, development, and deployment of machine learning solutions that improve patient outcomes and drive innovation and efficiency in healthcare. The Associate Machine Learning Engineer works under the guidance of senior engineers and data scientists to help build data pipelines, train and evaluate models (including natural language processing and generative AI use cases), and contribute to production\-ready code and documentation.* *Position is remote, but candidates based in Raleigh/Durham/Cary, NC or DC area preferred.*
Job Responsibilities:* Assist in developing and testing machine learning models and algorithms for healthcare use cases (e.g., risk stratification, prediction, decision support)
- Support data preparation activities including data cleaning, feature engineering, and basic data augmentation
- Help build and maintain repeatable ML workflows/pipelines for training, validation, and experiment tracking
- Run model evaluations using standard metrics; document results and support troubleshooting of performance issues
- Partner with software engineering/DevOps to help package, deploy, and monitor models in non\-production and production environments
- Create and maintain documentation, code comments, and basic runbooks; participate in code reviews and team ceremonies
- Support ML pipelines including data preparation, training workflows, and deployment into applications
- Stay current on advances in LLMs, GenAI, and compliance requirements (HIPAA, GDPR, FDA)
*The list of accountabilities is not intended to be all\-inclusive and may be expanded to include other education\- and experience\-related duties that management may deem necessary from time to time.*
Qualifications:
Required Qualifications* Bachelor's degree in Computer Science, Data Science, Engineering, or a related field (Master’s a plus).
- 2\+ years of relevant experience (including internships, co\-ops, research, or project\-based experience) building or applying machine learning solutions (preferably with proven applied AI/LLM deployments)
- Working knowledge of Python (or similar) and fundamentals of machine learning and statistics.
Preferred Qualifications* Exposure to machine learning frameworks/libraries (e.g., scikit\-learn, PyTorch, TensorFlow).
- Familiarity with SQL and common data tools (e.g., Pandas, NumPy).
- Basic understanding of NLP concepts; exposure to large language models (LLMs) and prompt engineering is a plus.
- Experience or coursework in MLOps concepts (e.g., version control, CI/CD basics, model monitoring, experiment tracking).
- Exposure to cloud platforms (Azure and/or AWS) for development or deployment is a plus.
- Familiarity with healthcare data concepts/standards (e.g., HIPAA awareness, HL7/FHIR, EHR data) is desirable but not required.
- Strong communication skills and willingness to learn in a multidisciplinary environment.
Why us?
We are a team of experienced and caring leaders, clinicians, pioneering technologists, and industry professionals who come together to redefine expectations for the healthcare industry. State and federal healthcare agencies, providers, and employers turn to us as their vital partner to ensure better healthcare and improve health outcomes. We do this through our people.
You will have meaningful work that genuinely improves people's lives across the country. We are a company that cares about our employees, and we give you the tools and encouragement you need to achieve the finest work of your career. Benefits
Benefits are a key component of your rewards package. Our benefits are designed to provide you with additional protection, security, and support for both your career and your life away from work. Our benefits include comprehensive health plans, paid time off, retirement savings, corporate wellness, educational assistance, corporate discounts, and more. Thank You!
We know your time is valuable and we thank you for applying for this position. Due to the high volume of applicants, only those who are chosen to advance in our interview process will be contacted. We sincerely appreciate your interest in Acentra Health and invite you to apply to future openings that may be of interest. Best of luck in your search!
\~ The Acentra Health Talent Acquisition Team
Visit us at https://careers.acentra.com/jobs EEO AA M/F/Vet/Disability
Acentra Health is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, disability, status as a protected veteran or any other status protected by applicable Federal, State or Local law. Experience in Lieu of Degree
For non\-clinical roles, or when not required by the contract specifically, the Company acknowledges that practical, hands\-on experience can provide skills and competencies equivalent to formal education. As such, in cases where a Bachelor's degree may be required, the Company will accept a minimum of six (6\) years of directly relevant professional experience in lieu of a degree. In instances where the candidate has an Associate's degree, the Company will accept a minimum of three (3\) years of directly relevant professional experience in lieu of the Bachelor's degree. Compensation
The pay range for this position is listed below.
“Based on our compensation philosophy, an applicant’s position placement in the pay range will depend on various considerations, such as years of applicable experience and skill level.”
Pay Range: USD $90,960\.00 \- USD $113,700\.00 /Yr.
Salary Context
This $90K-$113K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Acentra Health, 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($102K) sits 52% below the category median. Disclosed range: $90K to $113K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Acentra Health AI Hiring
Acentra Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $113K - $113K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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