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About This Role
Description
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About the Role
We are looking for an AI Engineer to join our growing AI team and help build intelligent, production\-grade AI systems that solve complex problems at scale.
In this role, you will work closely with product, engineering, and data teams to design, develop, and deploy AI\-powered applications, including LLM\-based solutions, AI agents, retrieval\-augmented generation (RAG), and intelligent automation workflows.
The ideal candidate is hands\-on, highly curious, and comfortable working across the full AI development lifecycle—from experimentation and prototyping to production deployment and optimization.
A Day in the Life
- You take an idea from paper to prototype to production. If you have only ever done one of those three, this role will stretch you, and we are fine with that if the rest is strong.
- You can build the model layer of a real product, not just a model. That means choosing model sizes, composing several models into a working system, and holding a product\-level accuracy bar.
- You write real code. Python fluently, PyTorch fluently, and enough systems sense to know why your training run is slow.
- You design experiments. You state the hypothesis, run the ablation, and report the result that disagrees with you.
- You measure things. You are suspicious of results that look good, and you build the eval before you build the model.
- You read current research and can tell the difference between a technique that will hold up and one that will not.
- You explain your work to people who are not AI engineers, including clinicians and operators who will tell you when your output is wrong.
What You Need
- MS or PhD in Computer Science, Machine Learning, or a related quantitative field. Exceptional BS candidates with substantial research or open\-source work will be considered.
- Depth beyond coursework: first\-author publications at NeurIPS, ICML, ICLR, ACL, EMNLP, or similar; meaningful open\-source ML contributions; a research internship at an AI lab; or models you trained and shipped that people actually used.
- You have fine\-tuned an open\-weight model yourself, understand the difference between parameter\-efficient and full fine\-tuning, and can explain why you chose one.
- Strong Python and PyTorch. Familiarity with the current training and serving stack (HuggingFace, FSDP or DeepSpeed, vLLM or SGLang, or equivalents).
- Some exposure to multi\-GPU training, even at lab scale. You should know what a sharding strategy is and why it matters.
- Evidence you can finish things.
We offer competitive benefits to set you up for success in and outside of work.
Here’s What We Offer
- Generous PTO Benefits: Enjoy PTO benefit accrual of 20 days per year.
- Parental Leave: Experience one of the industry's best parental leave policies to spend time with your new addition.
- Rewards \& Recognition: Unlock your potential and be rewarded generously with both monetary incentives and widespread recognition for your dedication and outstanding performance. Unlock your potential and be rewarded generously with both monetary incentives and widespread recognition for your dedication and outstanding performance.
- Insurance Benefits: We offer medical, dental, and vision benefits along with 100% company\-sponsored short and long\-term disability and basic life insurance. Legal aid and pet insurance options are available at a discounted rate.
Innovaccer is an equal opportunity employer. We celebrate diversity, and we are committed to fostering an inclusive and diverse workplace where all employees, regardless of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, marital status, or veteran status, feel valued and empowered.
Disclaimer: Innovaccer does not charge fees or require payment from individuals or agencies for securing employment with us. We do not guarantee job spots or engage in any financial transactions related to employment. If you encounter any posts or requests asking for payment or personal information, we strongly advise you to report them immediately to our HR department at [email protected]. Additionally, please exercise caution and verify the authenticity of any requests before disclosing personal and confidential information, including bank account details.
About Innovaccer
Innovaccer activates the flow of healthcare data, empowering providers, payers, and government organizations to deliver intelligent and connected experiences that advance health outcomes. The Healthcare Intelligence Cloud equips every stakeholder in the patient journey to turn fragmented data into proactive, coordinated actions that elevate the quality of care and drive operational performance. Leading healthcare organizations like CommonSpirit Health, Atlantic Health, and Banner Health trust Innovaccer to integrate a system of intelligence into their existing infrastructure— extending the human touch in healthcare. For more information, visit www.innovaccer.com.
Check us out on YouTube, Glassdoor, LinkedIn, Instagram, and the Web.
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 Innovaccer, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Innovaccer AI Hiring
Innovaccer has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
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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