Interested in this AI/ML Engineer role at Autonomize AI?
Apply Now →Skills & Technologies
About This Role
About Autonomize AI
Autonomize AI is an enterprise intelligence platform transforming how healthcare organizations access, orchestrate, and act on complex operational and clinical knowledge. Recognized as a World Economic Forum Technology Pioneer 2026, a Top 100 company on the Inc. 5000, and trusted by three of the five largest U.S. health enterprises, Autonomize AI combines healthcare-specific AI agents, workflow intelligence, and deep domain expertise to enable smarter decision-making across utilization management, care management, claims, pharmacy, and appeals. Backed by Valtruis, Asset Management Ventures, Cigna Group Ventures, ATX Venture Partners, and others. We're building the critical infrastructure for AI-native healthcare operations. We're growing fast and looking for bold, driven teammates to join us.The Opportunity
We're looking for a hands-on AI Engineer who's excited to ship real-world applications, not just benchmarks. You'll build and optimize the AI-native systems behind our healthcare agents and copilots, blending LLMs, vision models, and classical ML across structured and unstructured data to power decisions in high-stakes workflows. This role is for someone ready to go deep on applied problems, from retrieval to routing to generation, and push solutions into production fast.What You'll Do
- Build production AI pipelines. Own and optimize pipelines that combine LLMs and classical ML (RAG, extraction, scoring, summarization, classification) for utilization management, payment integrity, claims, and appeals.
- Extract from real-world documents. Implement VLM- and OCR-based pipelines that turn medical documents, faxes, and healthcare forms into structured, reliable data.
- Fine-tune and adapt models. Run SFT and parameter-efficient (e.g., LoRA) fine-tuning experiments on open-source and proprietary models.
- Implement retrieval and agents. Build retrieval strategies, prompt chains, tool-using agents, and inference orchestration (e.g., LangGraph) for production use cases.
- Build evals and analyze errors. Create eval harnesses and test sets, run error analysis, and turn findings into measurable accuracy gains.
- Monitor and improve. Track and improve model quality, latency, cost, explainability, and safety in production.
- Experiment fast. Prototype new techniques from recent research and help decide what's ready for production.
- Collaborate. Work closely with senior MLEs, product, engineering, and domain experts, and document your work clearly.
Qualifications
- 2+ years experience working in applied ML and LLMs.
- Strong Python skills and familiarity with PyTorch, Hugging Face Transformers, and LLM frameworks (LangChain, LangGraph, LlamaIndex).
- Comfort with embeddings, vector search, retrieval pipelines, and prompt engineering.
- Experience fine-tuning or adapting models, plus working knowledge of classical ML and NLP.
- Understanding of model evaluation, observability, and responsible AI practices.
- Experience deploying models to production and familiarity with MLOps tooling (MLflow, Docker, Kubernetes).
- Solid software engineering fundamentals and clean, testable code.
- A bias for experimentation, clarity, and shipping fast.
- Experience with healthcare, compliance-sensitive data, or regulated environments.
- A BS/MS in Computer Science, Engineering, Data Science, or a related field, or equivalent experience.
Nice to Have
- Experience with VLMs or document AI.
- Exposure to healthcare payer workflows (UM, claims, prior authorization, medical coding).
- Experience with agent frameworks or multi-step reasoning systems.
- Open-source contributions, side projects, or technical writing.
Who You Are
- Owner mentality. The buck stops with you. You own it, you learn it, and you get it done.
- Naturally curious. You'd rather experiment than hypothesize. You push boundaries and experiment your way through any problem.
- Passionate and unafraid. You're committed to the team and the mission.
- A team player. You love to learn and win together.
- A strong communicator. You're clear in voice, writing, chat, and video.
What We Offer
- Real-world impact. Your models run in production at some of the largest U.S. health enterprises, shaping decisions in prior authorization, claims, and appeals.
- Category-defining AI products. Help build the AI operating layer for healthcare, a space where the playbook is still being written.
- Hard, unsolved ML problems. Work on VLM document understanding over messy real-world faxes, agentic reasoning over clinical policy, domain fine-tuning (SFT, LoRA, RL), and models that learn from expert feedback in production.
- Research to production, fast. Read the paper, prototype it, ship it. New ideas reach customers in weeks, not quarters.
- Significant ownership and autonomy. Help shape AI strategy, not just execute it.
- Modern stack and compute. You'll have GPU resources and access to the latest open-source and frontier models.
- Build your public profile. Publish papers, file patents, write for our tech blog, and speak at conferences.
- Learn with strong peers. Grow through design reviews, paper discussions, and close collaboration with ML engineers and clinical experts.
- A high-growth environment with exceptional technical challenges.
- Competitive compensation with performance incentives.
- 100% employer-paid health, vision, and dental insurance.
- Retirement plans (401k), disability insurance, and employee assistance programs.
How to Apply
Send your resume and a brief cover letter to [email protected] explaining why you're the right partner for this mission.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Autonomize AI, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Autonomize AI AI Hiring
Autonomize AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.