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About This Role
Hi, I'm Andreas. Come build the new Nu Image® with us.
I'm the CEO of Nu Image®, and we're opening a new office in New Jersey. I'm looking for an AI engineer to be a founding member of it, and yes, the desk next to mine is open.
Open to current New Jersey residents only (on-site in Mahwah/Wyckoff, NJ). We are not offering relocation.
Here's the setup. We're building the next generation of our technology, and it's a small, tight core: you, working directly with our CTO, with AI agents as the force multiplier. You'll own the AI engineering and the hands-on code development.
AI agents do most of the typing here. But make no mistake: you'll own the code, not just prompt it. You'll understand the architecture, read every line the agents write, and be able to debug and fix it by hand when they get it wrong. When something breaks in production, "the agent wrote it" isn't an answer. You are the builder, and the result is yours.
And here's the line I care about most: I don't want someone who waits to be told what to do. See a problem? Fix it. See a better way? Build it and show us. Initiative is the job.
If you're the person at the meetup who can't stop talking about your agent setup, and you can also explain what's happening under the hood, keep reading.
Who we are
- Nu Image® is a US telehealth company. Founded in 2004, online since 2008.
- LegitScript certified, serving all 50 states.
- We're opening a new office in the Mahwah / Wyckoff area of New Jersey. This role is based there, fully on-site and in person. No relocation offered.
- Home page: https://nuimage.com
What you'll build (with our CTO)
- A modern web platform and internal tools that people actually enjoy using.
- Integrations and automation that connect systems and take busywork off people's plates.
- AI-powered workflows, plus the agent setups, guardrails and review steps that let a small core build like a much bigger one.
- Secure, reliable, well-tested systems with healthcare-grade privacy and security baked in from day one.
What a week roughly looks like
No two weeks are the same, but expect a mix of: - Agents running in parallel on branches while you review diffs, push back on bad code, and write the tricky parts yourself. - Working through architecture and priorities with our CTO, then turning them into shipped code. - Whiteboard sessions on new ideas, and turning the good ones into working prototypes. - Digging into a weird bug by hand: logs, queries, a debugger, and some stubbornness. - Spotting something nobody asked about (a slow query, a flaky job, a tool that would save us hours) and handling it. - Showing what shipped, what broke, and what you want to try next.
Perks of the role (just for fun; these are about the job, not benefits)
- Founding member of the NJ office. You'll help shape how it works.
- First dibs on the whiteboard.
- You pick the office coffee machine. Choose wisely.
- You get to name the agents.
- The CEO is one desk over, so your ideas get a fast yes or no.
- Your ideas don't sit in a backlog for six months.
Must-haves
AI (front and center) - You ship with AI coding agents daily: Cursor, the Grok ecosystem (including Grok Bot), and similar tools. You have a setup, opinions, and receipts. - You're current with today's AI tools and how they're changing month to month. - You've built real agent workflows: task breakdown, parallel agents, prompt and agent orchestration, and review loops that catch bad output before it ships. - You verify agent output: tests first, reading diffs line by line, and not trusting anything that just "looks right." - You keep sensitive data (customer information, secrets, credentials) out of prompts, logs, and tools that aren't approved for it.
Fundamentals (you own the result) - You can build and debug it yourself, without AI, when needed. Reading unfamiliar code, stepping through it, and fixing it by hand is normal for you. - Solid software architecture: you can explain why a system is shaped the way it is, and spot when an agent's design is going to hurt later. - Strong PHP and Laravel: queues, jobs, the scheduler, events, and Eloquent without the N+1 drama. - Real SQL skills (MySQL): schema design, indexing, migrations, reading query plans, and writing the query yourself. - Debugging: logs, stack traces, Xdebug or similar, reproducing issues, and finding root causes, not just symptoms. - Security and privacy best practices: authentication and authorization, input validation, encryption, least privilege, OWASP top risks, secrets handling.
The rest of the stack - Vue and Tailwind on the front end. - REST APIs: designing them and integrating with third-party ones. - Redis for queues, caching, and locks. - Cloud experience (AWS a plus). - Testing with Pest or PHPUnit. - CI/CD with GitHub Actions or similar. - Admin panel tools like Laravel Nova or Filament.
How you work - Initiative. You find the work instead of waiting for it. - Ownership. If you shipped it (or your agents did), it's yours to understand, fix, and improve. - You work well in a small core: direct with our CTO, clear about your reasoning, and happy to explain it to an agent or to a person. - You can show real, shipped work. We care about what you can do, not a years-of-experience number. - You currently live in New Jersey, within a reasonable commute of Mahwah / Wyckoff, and want to be in the office. We are not offering relocation or relocation assistance.
Nice-to-haves
- Infrastructure as code (Terraform, AWS CDK, Pulumi, or similar).
- Observability: structured logs, metrics, tracing, and alerts.
- You've built your own MCP servers, agent tools, or custom agent integrations.
- Evals for prompts and agents.
- Laravel Horizon, Octane, Pulse, or Reverb.
- Threat modeling and secrets management beyond the basics.
- Open-source work, side projects, or talks at meetups.
How to apply
Email [email protected] with the subject line "AI Engineer – NJ" and include:
- Your GitHub, portfolio, or anything you've shipped that we can look at.
- A short note (a few paragraphs is perfect) on the coolest thing you've built with AI agents. What it did, how you set up the agents, what went wrong, and how you fixed it. This is the part I'll read first.
- Where in New Jersey you live (town is fine).
No cover-letter templates, please. Write it the way you'd explain it to a friend.
Nu Image Medical, Inc. is an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other characteristic protected by federal, state, or local law.
Pay: $75,000.00 - $105,000.00 per year
Application Question(s):
- Do you currently live in New Jersey?
Ability to Commute:
- Mahwah, NJ 07430 (Required)
Work Location: In person
Salary Context
This $75K-$105K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Nu Image Medical, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($90K) sits 58% below the category median. Disclosed range: $75K to $105K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Nu Image Medical AI Hiring
Nu Image Medical has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Mahwah, NJ, US. Compensation range: $105K - $105K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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