Interested in this AI/ML Engineer role at Practice Flow?
Apply Now →About This Role
Company: Practice Flow
Location: Los Angeles, CA / Remote (Nationwide)
Role Type: Independent Partnership / Contract (1099\)
Compensation: Revenue\-share partnership (1099\) — no base, no cap. Two tracks: source and deliver, or deliver only. Projected on\-target earnings of 150,000–150,000–200,000 in year one, depending on your track and volume. These are model projections, not a guarantee. We walk through the full model on our first call.
Practice Flow is building a nationwide network of independent consultants who bring AI into private healthcare practices — without disrupting patient care.
About Practice Flow
Private practices are drowning in administrative bloat, burnout, and shrinking margins. The tools to fix this — generative AI, copilots, ambient scribes — already exist. Almost no one knows how to put them to work inside real clinical workflows.
Practice Flow is an AI Transformation Consulting firm that starts where the software companies stop. We work only with private healthcare practices, and we deliver hands\-on training built for how a practice actually runs. We don't sell software. We sell time, margin, and sanity back to clinicians and their teams.
The Role
We're looking for people who have run the practice — physician owners, practice administrators, clinical directors, and operations leaders — and who now want to help other practices make the leap to AI.
You lead the work in the room: hands\-on workshops, team build days, monthly AI clinics, and strategic advisory. Because you've sat in the operator's chair, you speak with clinicians and administrators as a peer, not a vendor — and that standing is what opens doors.
Think of it as running your own advisory practice without building one from scratch. You plug into Practice Flow's brand, methodology, and back office on day one, and you own the client relationship from the moment it becomes yours.
You are not an engineer, and you are not a beginner either. On heavier AI Turnkey builds, Practice Flow's technical team handles the integration work — you lead the engagement and stay the trusted face in the room. But in a workshop you are the one building the thing while the team watches, so you arrive already able to write a working prompt, stand up a no\-code automation, and talk about EHRs and the data moving between them without hand\-waving. That, plus the credibility of someone who has actually operated a practice, is the job.
What You'll Do
- Lead the transformation. Facilitate hands\-on AI training: half\-day workshops, full\-day team build days, recurring monthly clinics, and strategic advisory — built for how a real practice runs.
- Advise as a peer. Guide owners and administrators through decisions you've faced yourself — clinical workflow redesign, revenue cycle management (RCM), interoperability between systems, and what AI can and can't do for their bottom line.
- Drive growth *(track dependent)*. If you choose the Practice Track, your reputation and relationships in the healthcare community will start the conversations, backed by a curated list of target practices. If you choose the Delivery Track, you will step in to lead and onboard practices that our firm has already sourced and closed.
- Own the relationship. Carry your accounts through long\-term delivery, surfacing the next step as the practice's needs grow.
- Sharpen the craft with peers. Trade market intelligence, delivery wins, and referrals with other partners on the firm's regular calls. (Attendance is a resource, never a requirement — you control your schedule.)
Compensation \& Partnership Structure
We built this so the people creating the value keep most of it — a high\-leverage revenue share that grows as you do:
- You keep the majority. Every engagement pays you a high\-leverage revenue share that rises as you hit milestones. Practice Partners retain the maximum share for sourcing their own clients, while Delivery Partners accept a lower share in exchange for zero sales requirements. Practice Flow takes the remainder to run the machine behind you: billing, payment processing, client contracting, proven methodology, the firm's own corporate coverage behind every engagement, and lead generation.
- Get in early. We're in our founding chapter. The consultants who join now and produce lock in the strongest long\-term terms and share in the firm's success.
We share the full compensation model on our introductory call — that's the main thing the call is for.
Qualifications
Required
- Direct experience running or managing a healthcare practice — as a physician owner (MD/DO), practice administrator, clinical director, or senior operations leader.
- 8\+ years in healthcare, including real time in a leadership or management seat. This is a senior role.
- An advanced credential is a strong signal: MHA, MBA, MSN/RN leadership, MD/DO, or equivalent operating experience.
- Deep credibility with clinicians and administrators — you can hold a room as one of them.
- Hands\-on with everyday AI tools (ChatGPT, Microsoft Copilot, EHR\-embedded features) and able to explain them in plain, human terms.
- Able to build, not just discuss. You write prompts that work and can say why a bad one failed. You have built at least one no\-code workflow automation yourself and it ran. You understand how EHRs fit together and where a clinical or admin workflow actually breaks. We ask about this in the questionnaire, and it is a real bar rather than a nice\-to\-have.
- Self\-directed and entrepreneurial — ready to own a book of client relationships and grow it.
Preferred
- Prior consulting, advisory, training, or facilitation experience.
- An existing network of practice owners, managers, or operators — people who take your call.
- Working familiarity with HIPAA in clinical settings.
- Active ties to a focus specialty: dermatology, primary care, dental, physical therapy, mental health, or group practices.
Why This Role Might Not Be For You
We're straight about fit. This isn't for you if:
- You need a guaranteed base salary before you'll take action. (Even our Delivery Partners operate on a high\-leverage 1099 revenue share model.)
- You want to talk about AI rather than build with it. You will be the one at the keyboard in front of a clinical team.
- You'd rather not run a business. This is a 1099 partnership, so you operate through your own entity and carry your own professional liability policy, the way any independent practitioner does.
- You'd rather stay behind the scenes than be the trusted face in the room.
- You want a casual, low\-commitment side hustle rather than a practice to build and scale.
How to Apply
We're not collecting résumés — we're looking for partners. Complete our brief introductory questionnaire: https://forms.gle/AYpwcVTmEkE1srYQ7
It asks about the practices you've run, how you would map and instruct a clinical workflow automation, what you have built yourself, and which partnership track (Practice vs. Delivery) aligns best with your goals.
Strong candidates are invited to a strategic introductory call, where we share current market data, the full compensation structure, and the growth roadmap.
Pay: $150,000\.00 \- $200,000\.00 per year
Application Question(s):
- Have you completed the required introductory questionnaire at https://forms.gle/AYpwcVTmEkE1srYQ7? (If not, please complete it prior to submitting this application.)
Work Location: Remote
Salary Context
This $150K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Practice Flow, 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 in Demand for This Role
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. This role's midpoint ($175K) sits 19% below the category median. Disclosed range: $150K to $200K.
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.
Practice Flow AI Hiring
Practice Flow has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $200K - $200K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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