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About This Role
We are Local Infusion.
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Local Infusion is the fastest growing infusion provider in the United States, with a mission to transform the specialty infusion industry, because patients deserve better. By providing both exceptional, patient\-centered care and the proprietary, AI\-driven technology powering it, Local Infusion accelerates access, simplifies workflows, and improves outcomes for everyone in the infusion journey — from patients and clinicians to health plans, health systems, employers, and pharma.
Clinicians can spend less time on paperwork and more time with patients, bringing comfort, connection, and community back to healthcare. With Local Infusion, every patient and every care team is fully supported, every step of the way.
Fraud and Security Notice: Please note that all communication regarding job opportunities at Local Infusion will come exclusively from an @mylocalinfusion.com email address. If you receive messages from any other domain, please disregard them.
The Role
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Local Infusion is building a next\-generation operating system for infusion care — powered by automation, AI, and real\-time insights. We’re looking for a Machine Learning Engineer to own the intelligence layer of our platform. You’ll work across structured and unstructured data to drive automation, predict bottlenecks, and help patients start treatment faster.
Key Accountabilities:
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- Model Development: Build and deploy ML models to identify missing data, predict treatment delays, triage high\-risk referrals, and score payer/patient friction
- AI Integration: Architect and maintain LLM\-powered workflows
- Data Infrastructure: Build and maintain data pipelines to collect, clean, label, and store data across systems
- Model Monitoring: Define success metrics, monitor model performance over time, and iterate quickly to improve accuracy and reliability. Set up training data, data labeling workflows or feedback loops for model improvement.
- Cross\-Functional Partnership: Collaborate with product, engineering, and operations to identify high\-leverage automation opportunities and embed models into workflows.
Key Requirements:
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- 6\+ years in ML engineering, data science, or applied AI roles (bonus for healthcare experience).
- Proficient in Python (Pandas, Scikit\-learn, LangChain, PyTorch or TensorFlow) and SQL.
- Strong experience building and deploying models in production environments.
- Familiar with LLM integration, retrieval\-augmented generation (RAG), and vector search (e.g., Pinecone, FAISS).
- Skilled in working with unstructured data: OCR, NLP, form/document understanding.
- Experience with cloud infrastructure (AWS preferred), Git, and basic DevOps practices.
- Comfortable partnering with product and operations teams to solve real\-world business problems.
- Working with healthcare data (EMR/EHR, insurance claims, HL7/FHIR).
- Building patient or payer\-facing AI solutions in regulated environments (HIPAA).
The Local Infusion Way
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Local Infusion is a respectful, upbeat, and remote\-first team united by our mission of shaping the way specialty infusion care is delivered. We are highly ambitious but understand that in order to do a great job, we have to take care of ourselves; we expect that you will have time and energy devoted to your families, friends and hobbies.
As part of our team, full\-time employees get:
- Medical, dental, and vision insurance through our employer plan
- Short and long\-term disability coverage
- 401(k) — as an early\-stage startup, and we match!
- 15 Days PTO — and we want you to take it!
- Competitive paid parental leave and flexible return to work policy.
- We invest in your career. Our company is growing quickly, and we'll give you the opportunity to do the same. You'll have access to a number of professional development opportunities so that you can keep up with the company's evolving needs and grow your career along the way.
We don’t discriminate—Local Infusion is an Equal Employment Opportunity (EEO) Employer. We fundamentally believe that a more diverse and inclusive team leads to a stronger company more able to achieve our vision.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Local Infusion, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Local Infusion AI Hiring
Local Infusion has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Nashville, TN, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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