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: 100% performance\-based (1099\) — uncapped commission plus recurring income, no base, no cap. Projected on\-target earnings of $115,000–$165,000 in year one, compounding well beyond that as your recurring book matures.
Practice Flow is building a nationwide network of independent partners bringing AI into private healthcare practices.
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 a real clinical day.
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
This is a pure sales and business\-development role. You open the room, earn trust, and close the first engagement. It's consultative, peer\-to\-peer selling — no cold calling.
You won't run workshops or build workflows. When a deal is signed, delivery is handled by our delivery and technical teams. The relationship stays yours: you remain the client's primary contact and grow the account across everything we offer, for as long as they're with us.
If you've spent years building relationships with practice owners and administrators and want to put that network to work on something that pays you again and again, this is the role you want.
What You'll Do
- Open doors. Work your warm network alongside our prospect database — thousands of curated practices across our focus specialties — to start high\-level conversations.
- Run consultative discovery. Lead peer\-to\-peer conversations with practice owners, clinical administrators, and managing partners who live the operational bottlenecks every day.
- Close the entry engagement. Land the entry\-level workshop — the low\-friction, high\-value front door to every new relationship.
- Grow the account. Surface and close deeper engagements as the client's needs grow, from recurring monthly programs to larger AI builds and strategic retainers.
- Steward the relationship. Stay the constant contact while delivery rotates behind you, keeping your accounts active and expanding.
Compensation \& Partnership Structure
We built this to reward people who can both win clients and keep them — and to turn a one\-time sale into income that keeps paying:
- Uncapped commission. No base, no cap. You earn a competitive commission on the full value of everything you close, across every service, every time.
- Recurring income. This is the heart of the model. Much of what we sell is subscription\-based, and you earn on it every month for as long as the account stays active. Sell well and your income compounds instead of resetting to zero.
- Get in early. We're in our founding chapter. The partners 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
- 5\+ years selling into private healthcare practices, or firsthand experience running a multi\-provider clinical operation.
- A warm, existing network of practice owners, managers, or operators — people who will take your call today.
- Comfort with modern AI tools in a business context. You won't build anything, but you must speak credibly about what AI does for a practice's bottom line.
- Self\-directed and highly autonomous — you build and run your own pipeline without day\-to\-day management.
Preferred
- Active ties to a focus specialty: dermatology, primary care, dental, physical therapy, or mental health.
- Consultative sales background — solution selling, not product pitching.
- Working familiarity with HIPAA in clinical settings.
- Experience with recurring\-revenue models and long\-term account management.
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 or spoon\-fed leads before you'll take action.
- You want to deliver the engagements you close, not hand them off.
- You don't have real, leverageable relationships in healthcare.
- You want a casual side hustle rather than a book of business 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/9sy2ML3vzvxwQArW8
It asks where your healthcare relationships come from, the network you'd bring, and why an independent, performance\-based partnership is the right move for you right now.
Strong candidates are invited to a strategic introductory call, where we share current market data, the full compensation structure, and the growth roadmap.
Pay: $115,000\.00 \- $165,000\.00 per year
Application Question(s):
- Have you completed the required introductory questionnaire at https://forms.gle/9sy2ML3vzvxwQArW8? (If not, please complete it prior to submitting this application).
Work Location: Remote
Salary Context
This $115K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($140K) sits 36% below the category median. Disclosed range: $115K to $165K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Practice Flow AI Hiring
Practice Flow has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $165K - $200K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.