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About This Role
Parachute Health is transforming post\-acute care as the leading digital ordering platform for medical equipment and supplies. We connect major health systems, health plans, and suppliers to help patients get the life\-saving products they need at home. Since launching, we've connected 300,000\+ clinicians and 3,000\+ supplier locations across all 50 states and helped 15M\+ patients. What started as a DME ePrescribing tool has become the order management platform of choice for home medical equipment.
Join our team and make a difference in patient care.
### About the Role
As Director of AI Engineering at Parachute Health, you will be a dedicated AI builder. This is a hands\-on engineering role, not an advisory or program\-management one: you will find the places where AI\-native systems can remove operational waste, then personally design, build, and deploy them. You will write production code, wire up agentic systems, and be accountable for systems that are live and running across the organization.
You will lead a small pod of 2\-4 engineers that embeds with business functions across the company, ships AI\-first solutions, and equips each team to maintain what you've built before moving on to the next opportunity. Your impact will be measured in throughput and leverage gained by those functions.
### What you'll do
- Design, Build, Harden and Deploy End\-to\-End: Personally ship agentic solutions from scoping the problem and selecting the right tools through production code, orchestration layers, testing, and handoff to a maintained system.
- Own the AI Automation Roadmap: Within a transformation engagement, identify and prioritize automation opportunities within internal operations (supplier onboarding, clinician activation, sales, finance, and support) and sequence a roadmap of the highest\-leverage builds.
- Lead the Pod as a Player\-Coach: You are the first engineering hire of the AI transformation team. Directly manage a team of 2\-4 engineers while staying hands\-on in the work. Set the pace, hold the code quality bar, and model the velocity standard: days, not weeks.
- Turn Problems into Systems: Conduct discovery with internal teams, map current\-state workflows, find the highest\-leverage intervention points, and design, build, test and hip end\-to\-end AI\-first processes.
- Scout the Tooling Frontier: Influence company\-wide decisions on new models, orchestration frameworks, agent toolkits, and workflow platforms, and bring the right ones to Parachute's specific operational challenges.
### What we're looking for
- Software Engineering Foundation: You understand system design, deployment, APIs, data flow, and the full SDLC, and you apply that rigor to AI systems. You write and ship production code, not just prompts.
- Agentic Fluency and Engineering Depth: You have hands\-on experience with RAG pipelines, vector databases, LLM orchestration (LangChain, LangGraph, or equivalent), voice\-agent infrastructure, MCP, and multi\-agent coordination. You know when to use each and why.
- Hands\-On Builder: You have personally built and deployed AI\-powered systems that drove real operational outcomes, not only managed the team that built them. You can point to concrete throughput or efficiency gains.
- Bias for Speed and Impact: You ship meaningful systems with speed \- fully enabled by the latest AI coding tools.
- Structured Communicator: You can walk a non\-technical stakeholder through what you're building and why it will work, probe their problem to find the real constraint, and translate their feedback into precise technical requirements.
- Player\-Coach: You've built and led small, high\-output technical teams. You know how to hire, onboard, and develop engineers while staying in the work yourself.
- Nice to Haves: Deep ML experience; business transformation and change\-management acumen.
### Benefits
- Medical, Dental, and Vision Coverage:Comprehensive plans with options for low\-to\-no\-cost premiums.
- Employer HSA Contribution: Company\-funded contributions to your Health Savings Account.
- 401(k) Retirement Plan
- Equity Incentive Plan
- Annual Company\-Wide Bonus: Opportunity for up to 15% bonus based on company performance.
- Remote\-First Culture:We are remote\-first with a dedicated NYC office and reimbursement options for co\-working spaces.
- Flexible Vacation Policy
- Summer Fridays:5 additional Fridays off during the summer (separate from PTO).
- Home Office and Wellness Stipend
- Monthly Internet Stipend
- Annual Learning and Development Stipend
Base Salary Band (based on experience and level)
$240,000\-$300,000
*California job applicants may access the Notice of Collection of Personal Information and Privacy Policy with information and rights required by the California Privacy Rights Act (CPRA) the link* *here**.*
*We are proud to be an equal opportunity employer that does not discriminate on the basis of actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth related medical conditions and lactation), gender identity or gender expression (including transgender status), sexual orientation, marital status, military service and veteran status, disability, genetic information, or any other characteristic protected by applicable federal, state, or local laws and ordinances.*
*This role is not eligible for employer visa sponsorship. Applicants must be legally authorized to work in the United States at the time of application and for the duration of employment. The Company does not sponsor employment authorization for this position.*
Salary Context
This $240K-$300K range is above the 75th percentile 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 Parachute 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($270K) sits 26% above the category median. Disclosed range: $240K to $300K.
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.
Parachute Health AI Hiring
Parachute Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $300K - $300K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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