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About This Role
At Curai, we believe that access to high\-quality healthcare is a fundamental human right, not a privilege. Our mission is to radically transform healthcare delivery by harnessing the power of artificial intelligence and clinical expertise to make care more affordable, accessible, and effective for everyone. Patients interact with advanced AI systems at every step of their care and follow\-up. Licensed physicians review each case with the patient for the clinical decision in our integrated virtual clinic.
We ground our approach in rigorous research, continuous learning, and a deep commitment to clinical integrity. We focus on making a real impact: improving health outcomes, expanding access to care, and setting new standards for what trustworthy, patient\-centered healthcare is. Read some of our latest publications.
### About the Staff Applied AI Engineer role
We are hiring Staff Applied AI Engineers across a range of seniority levels to push the frontier of applied AI in healthcare. As an MTS on the engineering team, you will design, build, and ship ML and LLM systems that directly shape how clinicians and patients interact with our platform. You will work end\-to\-end: framing the problem, exploring data, training and evaluating models, productionizing inference, and measuring real\-world clinical and product impact. The bar is high and the surface area is large; we are looking for engineers who want significant ownership and are excited to operate where research meets production.### What You'll Do
- Define technical strategy across multiple AI initiatives, driving architectural decisions, influencing product and research direction, and aligning engineering investments across teams to maximize long\-term business and clinical impact.
- Design, build, train, evaluate and improve advanced machine learning and LLM\-based systems for patient and provider\-facing products (e.g., conversational AI, personalization, user understanding, clinical decision support, chronic care management).
- Own problems end\-to\-end: scope the problem with clinicians and product partners, build datasets and evaluations, iterate on modeling, and ship to production with the right monitoring and guardrails.
- Develop robust evaluation frameworks — offline benchmarks, human\-in\-the\-loop review, online experiments — that give us confidence our models are safe, accurate, and improving over time.
- Build and improve the platform that lets the team move quickly: data pipelines, training and inference infrastructure, prompt and model management, and tooling for clinical reviewers.
- Partner closely with clinicians, product, and engineering to translate medical and operational requirements into ML problems and ship measurable improvements to patient and clinician experience.
- Set technical direction for your area, mentor other engineers, and raise the bar on engineering and scientific rigor. The scope of leadership scales with seniority.
- Stay close to the literature and the rapidly evolving AI ecosystem; bring back what is most useful for our patients and our team.
### What You'll Bring
- Bachelor’s degree in Computer Science, Software Engineering, Math, or other related technical degree
- 5\+ years of hands on engineering experience with 2\+ years building and deploying machine learning systems including generative AI (LLMS), and a clear track record of impact.
- Strong software engineering fundamentals and the ability to ship reliable, well\-tested code in Python (or a comparable language) in a production environment.
- Practical understanding of modern LLM techniques: prompting, retrieval\-augmented generation, fine\-tuning, evaluation, and the trade\-offs between them.
- Comfortability working with messy, real\-world data and designing evaluations to know whether a system is actually working.
- Strong written and verbal communication; ability to cross\-collaborate with clinicians, product managers, and engineers across disciplines.
- A bias toward action and ownership: you can take an ambiguous problem, drive it to a result, and bring others along.
- Care for the mission. You want your work to translate into better health outcomes for real patients.
Nice to have
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- Experience applying ML or LLMs in healthcare, life sciences, or another regulated, high\-stakes domain.
- Experience with clinical NLP, medical knowledge representation, or working with electronic health record data.
- Experience building agentic systems, tool\-using LLMs, in production.
- Experience scaling ML infrastructure — training pipelines, distributed inference, evaluation platforms — for a small, fast\-moving team.
- Track record of technical leadership: setting direction across teams, mentoring engineers, or publishing influential work.
### What We Offer
- High ownership work on problems that matter, with a tight feedback loop from real clinicians and patients.
- A small, senior team where your work shows up in the product quickly.
- Competitive compensation, meaningful equity, and comprehensive benefits.
- Remote\-first, flexible work environment across the U.S.
Compensation: $200,000 \- $240,000/year
The pay range listed for this position reflects what Curai Health reasonably and in good faith expects to pay at the time of posting. Actual base salary will depend on a variety of factors, including your qualifications and years of relevant experience.
Culture: A mission\-driven team of talented colleagues who are committed to living our values, collaborating closely, and driving meaningful impact in healthcare.
Benefits:
- Comprehensive medical, dental, and vision coverage
- Flexible spending plans
- Generous and flexible Paid Time Off (PTO), floating holidays, and parental leave
- 401k plan with employer matching
- 100% remote — work from home
Curai Health is an equal opportunity employer and is deeply committed to building a diverse and inclusive workforce. In keeping with our beliefs and values, no employee or applicant will face discrimination or harassment based on race, color, ancestry, national origin, religion, age, gender, marital or domestic partner status, sexual orientation, gender identity, disability status, veteran status, or any other legally protected characteristic. To promote an equitable and bias\-free workplace, we set competitive compensation packages for each position and do not negotiate on our offers. We are looking for mission\-driven teammates who embody our core values and appreciate our transparent approach.
*Beware of job scam fraudsters! Our company uses @curai.com email addresses exclusively. We do not conduct interviews via text or instant message and we do not ask candidates to download software, to purchase equipment through us, or to provide sensitive personally identifiable information such as bank account or social security numbers. If you have been contacted by someone claiming to be from Curai from a different domain about a job offer, please report it as potential job fraud to law enforcement and contact us at* *jobs@curai.com**.*
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $200K-$240K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Curai 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $200K to $240K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Curai Health AI Hiring
Curai Health has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $200K - $240K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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