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About This Role
SENIOR MACHINE LEARNING ENGINEER IV
At Trilliant Health, we help hospitals and healthcare systems make smarter, data-driven decisions in an increasingly complex healthcare environment.
Our teams combine healthcare data, analytics, machine learning and AI to create products that help healthcare leaders understand what is happening in their markets and act with confidence.
About the Role
We are seeking a Senior Machine Learning Engineer IV to join our Machine Learning (ML) team and help build the models, systems, and software that power Trilliant Health’s machine learning and AI capabilities.
This is a senior individual contributor role combining hands-on machine learning, software engineering, and technical leadership. You’ll work with complex healthcare data to develop production-ready ML solutions and help shape the technical direction of our ML engineering organization.
We value engineers who are curious, proactive, collaborative, and able to communicate complex technical concepts clearly to both technical and non-technical audiences. We prefer simple solutions over complex ones, and we document what we build and how it works.
Our Stack
Our stack is built around Python, which we manage with uv in a monorepo. Most of our heavy computation runs as jobs on Kubernetes, where we increasingly favor DuckDB and Parquet, though some of our pipelines still run on Spark and Databricks.
We build our models with libraries including scikit-learn, XGBoost, and PyTorch, and our newer LLM-based systems with LangChain. Our services and internal web applications use FastAPI and NiceGUI, several of them backed by SQLite, and we deploy everything to Azure.
Every change runs through pre-commit hooks, static type checking, and automated tests before it merges. We also publish several of our core libraries as open source on PyPI and GitHub.
What We’re Looking For
Education & Experience
- 8+ years of professional experience in machine learning, software engineering, data science, or a closely related technical discipline, with significant hands-on experience building production ML systems
- Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Mathematics, Statistics, or a related field or equivalent professional experience
- Demonstrated experience taking ownership of significant ML initiatives and influencing technical decisions across teams
- Demonstrated willingness to speak up, challenge assumptions, and advocate for sound technical or modeling decisions.
- Experience working collaboratively across engineering and cross-functional teams
Machine Learning & Data Science
- Strong understanding of predictive modeling and machine learning techniques
- Experience selecting, developing, evaluating, and improving ML models
- Understanding of statistics, experimentation, and model evaluation
- Experience working with large, complex datasets
- Experience developing ML workflows from experimentation through production
Software Engineering
- Strong, hands-on Python development experience, including writing production-quality, maintainable, and well-tested code
- Experience developing and maintaining distributed data pipelines
- Experience with SQL and relational databases
- Experience with CI/CD, Python packaging, and software deployment
- Proficiency with Git and modern version-control practices
- Strong familiarity with Linux and command-line tools
- Ability to write clean, reusable, maintainable, and well-tested code
- Experience using AI coding agents and reviewing, testing, and improving agent-generated code
Nice to Have
- Experience building, evaluating, or deploying LLM-based or other AI systems
- Experience with Healthcare claims data
- Experience with entity resolution/record linkage
- Experience with Spark or similar compute frameworks
- Open-source contributions
If you don’t meet every requirement, we’d still love to hear from you.
Why Join Trilliant Health?
You’ll have the opportunity to work at the intersection of healthcare, data, software engineering, and machine learning, building technology that helps healthcare organizations make better decisions.
You’ll also have a meaningful opportunity to influence technical direction, work on challenging data problems, and collaborate with a highly talented team that values curiosity, ownership, deep listening, and effective communication.
Benefits
- Comprehensive health benefits package
- 401(k) plan
- Flexible PTO
- Equity
Please Note
Trilliant Health is unable to provide visa sponsorship for this position.
How to Apply: If this opportunity sounds like a good fit, we’d love to hear from you! Please submit your application directly through Indeed to be considered for this position.
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Referral program
- Retirement plan
- Vision insurance
Work Location: Remote
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Trilliant 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Trilliant Health AI Hiring
Trilliant Health has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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