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About This Role
About You
We’re seeking a senior technical leader whose background combines substantial AI/ML research expertise with hands-on engineering experience. You’ll help shape the architecture and technical direction of our intelligent systems. The remit extends beyond implementation as an individual contributor: you’ll uncover unanswered questions, develop experiments to investigate them, and take ownership of AI development from initial research through production.
Your experience should combine research depth, typically gained through a PhD or equivalent industry research, with substantial work making AI systems dependable on complex, real-world production data. You’ve created evaluation frameworks, investigated LLM failure modes, and learned to distinguish impressive benchmark results from meaningful evidence of reliability. You bring informed perspectives on measuring AI performance and deploying systems safely in environments where the consequences matter.
AI is central to both our products and our working practices. Across research, engineering, and operations, we make extensive use of AI tools to accelerate execution and support clearer thinking. Everyone is expected to adopt this approach. At a senior level, that means demonstrating how AI can amplify informed judgment without replacing it.
Healthcare offers both extensive data and significant responsibility. We’re looking for someone who understands the importance of both.
Your Responsibilities
AI Strategy & Research
- Own the technical strategy for AI/ML across our clinical intelligence products, covering information extraction, summarization, reasoning, and retrieval. Carry that strategy through to implementation.
- Assess weaknesses in existing systems, including inconsistent performance, missing coverage, and evaluation methods that overlook failures encountered in practice.
- Follow developments in LLMs, clinical NLP, retrieval-augmented generation, RLHF/RLAIF, agents, and structured prediction, bringing relevant advances into production.
- Establish how we evaluate and benchmark models, prioritizing measures relevant to patient care rather than metrics selected simply because they are straightforward to collect.
Production Systems & Engineering
- Develop and enhance production pipelines for clinical entity extraction, summarization, chart abstraction, and structured inference from unstructured clinical text.
- Shape and refine our use of RAG, prompt engineering, fine-tuning, and model selection, applying rigorous evaluation throughout.
- Oversee secure AI deployment that complies with HIPAA and SOC 2, focusing on accuracy, auditability, and the risks associated with hallucinations.
Technical Leadership & Collaboration
- Provide technical guidance on AI decisions, helping the team determine which approaches to explore, diagnose problems, and assess whether models are ready for release.
- Partner directly with clinical subject matter experts to understand how outputs support clinical workflows, where they fall short, and how quality should be defined in practice.
- Mentor engineers and strengthen the team’s understanding of AI/ML best practices, including effective use of AI tools in everyday technical work.
Required Experience & Skills
Essential Requirements
- Substantial expertise in modern AI/ML, LLMs, NLP, information extraction, or a closely related discipline, supported by direct work with leading approaches rather than theoretical familiarity alone.
- A PhD in ML, NLP, computer science, or a related discipline, OR comparable research depth evidenced by publications, substantial open-source contributions, or impactful AI work in industry.
- Experience creating and owning evaluation frameworks, including defining what to measure, accounting for Goodhart’s Law, and identifying regressions, rather than only executing existing evaluations.
- Demonstrated experience progressing AI systems from research or prototypes into dependable production deployments, with a clear understanding of their limitations and the causes of failure.
- At least 8 years of software engineering experience, predominantly focused on AI/ML systems.
Technical Expertise
- Fluency in Python, either PyTorch or JAX, and the core ML tooling used for training, fine-tuning, inference, and quantization.
- Practical experience working with LLMs through prompting strategies, RAG architectures, RLHF/preference tuning, instruction tuning, or model distillation.
- Strong data capabilities spanning SQL, pipeline design, annotation tools, and dataset curation, alongside an understanding that data quality underpins much of AI performance.
- Experience using NLP libraries such as Hugging Face and spaCy, together with production model-serving infrastructure such as vLLM, TGI, or equivalent tools.
- Skilled use of AI-assisted development tools such as Claude Code, Cursor, Copilot, or similar. You actively guide and scrutinize these tools rather than accepting their responses without challenge. You understand when to explore further, when to reject an output, and how to organize tasks to obtain useful results without overlooking subtle errors.
Working Approach
- An AI-first approach grounded in practical understanding of where AI provides meaningful benefits and where it does not. This should be reflected in your coding, research, and daily problem-solving.
- The judgment to recognize when a model should not be released, balanced with the pragmatism to deploy imperfect systems where risks are understood and contained.
- Comfort navigating open-ended research questions, combined with the discipline needed to turn uncertain findings into production systems.
- Genuine interest in the clinical domain and recognition of the responsibility involved in developing high-stakes AI.
Additional Experience
- Familiarity with clinical NLP, healthcare data standards such as HL7 and FHIR, ICD/CPT coding, or previous healthcare AI work.
- Experience in structured prediction, building medical knowledge graphs, or extracting information from clinical data.
- Publications or applied research contributions covering NLP, LLMs, clinical AI, or evaluation methods.
Technology Environment
Python · PyTorch · Hugging Face · PostgreSQL · BigQuery · dbt · FastAPI · Google Cloud Platform · LLM APIs (OpenAI, Anthropic, etc.) · Jupyter / Streamlit
Salary Context
This $250K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Storm3, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($275K) sits 30% above the category median. Disclosed range: $250K to $300K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Storm3 AI Hiring
Storm3 has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span TN, US, CA, US, New York, NY, US. Compensation range: $265K - $350K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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