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About This Role
We unlock ground truth for the enterprises that power the global economy.
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There are billions of important conversations taking place in real time each day in hospitality, food service, retail, logistics and countless other industries \- conversations with employees and conversations with customers. Until today, those conversations vanished into thin air the second they happened.
Arbor turns in\-person conversations into executive\-grade strategic intelligence. We're closing enterprise contracts at companies in some of the most vital industries of the world. This problem is huge and there’s nothing quite out there to solve it. Come join us if you're a product builder who gets this space!
### The Role
This is a foundational engineering hire. You'll report directly to our CTO and co\-founder, and work shoulder\-to\-shoulder with him on the core product. Umi, our AI researcher, runs natural voice interviews with frontline employees and customers at scale, then turns thousands of conversations into intelligence leaders act on.
It's voice, LLM applications, and data pipelines in one product.
This role covers the best parts of the modern AI stack, and your fingerprints will be on all of it.
### What You'll Do
- Build real\-time voice agents (LiveKit, Pipecat, Twilio, Deepgram, ElevenLabs) that hold natural interviews at scale
- Design agentic LLM workflows and agent harnesses on OpenAI, Anthropic, and Google Gemini models: prompts, structured outputs, and evals
- Ship full\-stack features end\-to\-end in Python/FastAPI and TypeScript/React
- Build pipelines that turn thousands of conversations into themes, quotes, and recommendations
- Make non\-deterministic systems behave in production: observability, evals, guardrails
- Help shape engineering culture, tooling, and architecture as one of the first engineers
### What We're Looking For
- Strong engineering fundamentals and experience shipping production systems end\-to\-end
- Hands\-on LLM experience (prompting, structured output, retrieval, evals) beyond demos
- Proficiency in Python and TypeScript, comfort across backend and frontend
- Bias to ship: you own problems end\-to\-end and iterate fast with real users
- Care for the humans behind every signal
### Nice to Have
- Real\-time voice or audio experience (LiveKit, Pipecat, WebRTC, telephony, STT/TTS)
- Data pipeline or analytics engineering experience
- Early\-stage startup experience or 0\-to\-1 product ownership
### Benefits
- Meaningful equity and competitive pay
- Medical, dental, and vision for you and your dependents
- Flexible time off and supportive parental leave
- Bright NYC office, team meals, offsites, and customer travel
- Learning budget and top\-tier gear
Why Join Arbor:
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- Huge market, weak competition: Billions of offline conversations happen daily. No one else is solving this for real enterprise.
- Large enterprise customers: We're already working with multi\-billion dollar manufacturers, retailers, and logistics companies.
- Strong founding team: Backgrounds from Harvard, Princeton, Meta, Insight Partners, IBM. We know how to listen, build, sell, ship, scale \- then iterate and accelerate.
- Real upside: Already backed by the best with $6M\+ from 645 Ventures, NextPlay Ventures (Jeff Weiner), Wisdom, and angels, but early enough for founding team members to see generational outcomes from equity.
- Product that works: Customers see immediate ROI. Enterprise deals close fast.
Mostly importantly, join for the team. We're A\+ players who live our values:
- Own the outcome: From first idea to customer impact, see it through. No hand\-offs, no excuses.
- Real problems only: Find energy in work that actually matters, not in building features that sound cool but solve nothing.
- Intellectual honesty: Admit what isn't known, ask uncomfortable questions, and challenge assumptions without fear.
- Clarity through chaos: Thrive in 0\-to\-1, high\-intensity environments. Move fast but think clearly, turn confusion into direction, ship quality under pressure.
- Empathy above all: Obsess over customer problems and think deeply about user needs before making any decision.
Compensation Range: $150K \- $220K
Salary Context
This $150K-$220K range is above 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 Arbor, 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 $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 ($185K) sits 15% below the category median. Disclosed range: $150K to $220K.
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.
Arbor AI Hiring
Arbor has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $220K - $220K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 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
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