Interested in this AI/ML Engineer role at David Joseph & Company?
Apply Now →About This Role
New York City, NY · On\-site (min 3 days/week) · Full\-time
Compensation: $110,000–$180,000 base \+ variable tied to upsell
### About the Company
Our client is a healthcare AI company building voice agents that automate appointment scheduling, inbound call handling, and patient\-provider communication so clinical staff can spend more time on care. They have strong product\-market fit in their home market — hundreds of practices live and growing fast — and are now standing up their US operation from a New York office, backed by fresh seed funding and already signing their first US customers.
Founded 2024 · 11–50 people · Industry: Healthcare / AI
### The Role
This is a founding post\-sale role: you'll own everything that happens after a customer signs — implementation, deployment, customer success, and expansion — and build the US operations function from the ground up. You'll report directly to the CEO, own net revenue retention as your core metric, and grow the function into a small team over your first year. Early success looks like getting the first wave of US accounts deployed and in production and turning your work into a repeatable onboarding playbook.
What you'll be doing
- Own the full post\-sale lifecycle for US customers — configure and deploy signed accounts into production, drive fast time\-to\-go\-live, and keep deployment success high.
- Act as the primary point of contact for customer success, support, and account management so the sales team stays focused on new business.
- Drive retention and expansion against a personal upsell quota; net revenue retention is the metric that matters most.
- Build and document a repeatable US onboarding and deployment playbook.
- Get deep enough in the product to configure and go live independently, working with integrations and no\-code tooling.
- Hire and lead a small operations team as the customer base grows.
Tech stack: AI voice agent platform, product integrations, and no\-code tooling; comfort with AI coding tools is a plus.
### Requirements
- 3–5 years in a customer\-facing deployment, implementation, or customer success role at a fast\-moving startup or tech company.
- Strong product instincts and technical comfort — happy configuring software and working with integrations, and quick to master new products (no engineering background required).
- Excellent client\-facing communication; able to build trust with operators and end users and represent the company well.
- Highly organized and a strong problem\-solver, comfortable owning a customer portfolio and juggling several workstreams at once.
- Entrepreneurial and self\-directed, at ease building without a ready\-made playbook in an early\-stage setting.
- Based in New York City and able to work on\-site at least three days a week.
### Nice to Haves
- Time at an early\-stage SaaS company, ideally in a deployment, implementation, or operations role.
- A healthcare background or familiarity with how clinical practices operate.
- Experience at a high\-talent technology organization known for its bar on people.
- A strong university background.
- A track record of building things — side projects, products, or other entrepreneurial initiative outside a formal role.
- Hands\-on with modern AI coding tools.
### Why Join
- Founding ownership of a brand\-new US function, reporting straight to the CEO.
- A product with real, fast\-growing traction in its home market, now expanding into the US.
- Genuine scope and upside — own retention and expansion, carry variable comp on upsell, and build and lead your own team.
### Details
- Location: New York City, NY
- Work policy: On\-site, minimum 3 days/week (ideally 5\)
- Compensation: $110,000–$180,000 base \+ variable
- Visa sponsorship: Not available
- Employment type: Full\-time
Salary Context
This $110K-$180K range is below 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 David Joseph & Company, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($145K) sits 33% below the category median. Disclosed range: $110K to $180K.
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.
David Joseph & Company AI Hiring
David Joseph & Company has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Positions span San Francisco, CA, US, New York, NY, US. Compensation range: $180K - $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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.