Interested in this AI/ML Engineer role at Elise A.I. Technologies Corp.?
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Location
New York City
Employment Type
Full time
Location Type
On-site
Department
Product
About EliseAI
At EliseAI, we're improving the industries that matter most: housing and healthcare. Everyone needs a place to live and access to quality healthcare, yet both are often harder to secure than they should be.
By integrating AI agents deeply into existing workflows, we make them more efficient, reduce costs, and improve the experience for everyone.
- Housing: We simplify how renters tour apartments, sign leases, submit maintenance requests, and stay connected with their property team—bringing everything they need for their home into one place.
- Healthcare: We make it easy to schedule appointments, complete intake forms, and we help patients communicate with providers, so everyone can focus on health instead of paperwork.
With EliseAI, organizations reduce manual work, improve accessibility, and deliver a seamless experience across essential services. We recently raised a $250 million Series E round led by Andreessen Horowitz to accelerate this mission.
The Future Platforms Team - Executing on EliseAI’s Future Products
The Future Platforms team is responsible for executing on the next generation of AI products and business lines. This team will blend go-to-market, product, operations, and strategy with a mandate to push the frontier of AI and own everything required to take them from idea to market leadership.
Our goal is to take Elise from agentic AI to truly end-to-end autonomous operations, fundamentally transforming how housing operators run their businesses and how residents experience their homes.
Future Platforms will operate as a startup within a startup, run like a Seed or Series A company but with the capital, resources, and market access of a well-funded Series E, deep C-suite involvement (CEO, CRO, CSO, CTO) and founder-level ownership from day one.
About The Role
EliseAI is expanding its platform across housing, and we’re looking for an AI Deployment PM to help bring new products to customers. You’ll work closely with Product and Engineering to own the work between a customer saying yes and a successful launch.
This is a hands-on role: you’ll drive deployments alongside customers and internal teams, work through complex customer data, and identify issues that need to be resolved before a product goes live. Our deployment processes are still taking shape, so you’ll also help establish a clearer, more repeatable way to launch as we grow.
Key Responsibilities
- Own customer deployments from initial discovery through launch, keeping work on track and resolving blockers
- Work directly with customer teams, including operations leaders, accounting controllers, and property managers, to understand their processes and requirements
- Analyze customer data, identify discrepancies, and ask the questions needed to determine how information should flow into EliseAI’s products
- Partner with Product and Engineering to investigate issues, set priorities, and turn what you learn during deployments into product improvements
- Build practical processes and documentation that make future deployments faster and more reliable
- Identify and communicate progress, risks, and decisions clearly to customers and internal stakeholders
- Attract top-tier talent to join our driven team
Move at rocket speed, build something massive.
We’re scaling fast, solving real client problems with precision and ambition. Here, you own your impact; full autonomy, no micromanagement, no fluff.
We hire the best, expect the best, and give you the masterclass of your career. It’s hard, it’s intense, and it’s the most rewarding work you’ll ever do. If you’re hungry, driven, and ready to build something massive, climb aboard.
Requirements
- 2-4 years of experience in a role involving analytical problem-solving and ownership of complex projects. Experience in consulting, banking, data analytics, or an early-stage startup could be a strong fit
- Strong analytical skills and comfort working directly with spreadsheets and imperfect data
- Ability to spot patterns, get to the root of a problem, and make progress when the path forward is unclear
- Clear communication skills and confidence working with customers and cross-functional teams
- A self-starter who takes ownership, follows through on details, and is willing to do hands-on work to make a deployment successful
- A degree in statistics, mathematics, computer science, or a related technical field is a plus
- Willingness to work in person at our office 4-5 days a week
Why Join
Growth and impact. It’s not often that you can get in on the ground floor of a funded (unicorn!) startup that’s scaling so fast. That means that instead of following a playbook, you’ll be writing it. Every single day you will be challenged to identify how we can scale and execute on it. You’ll learn what works when you succeed and what doesn’t when you fail. Either way, the rest of the team will be here to support you.
Benefits
In addition to the growth and impact you’ll have at EliseAI, we offer competitive salaries along with the following benefits:
- Equity in the company
- Medical, Dental and Vision premiums covered at 100%
- Fully paid parental leave
- Commuter benefits
- 401k benefits
- Fitness & home services stipend to cover part of your expenses so you can focus on what matters
- A collaborative in-office environment with an open floor plan, fully stocked kitchen, and all meals covered in the office
- Unlimited vacation and paid holidays
- We'll cover relocation packages and make the move exciting, not painful!
Job Compensation Range
The salary range for this role is $150,000 - $250,000. EliseAI offers a competitive total rewards package which includes base salary, equity, and a comprehensive benefits & perks package. Exact compensation is determined based on a number of factors including experience, skill level, location and qualifications which are assessed during the interview process. Additional details about total compensation and benefits will be provided by our Recruiting Team during the hiring process.
EliseAI provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any kind without regard to race, color, religion, age, sex, national origin, disability status, genetic information, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable federal, state, or local law.
Employment with EliseAI is on an “at-will” basis, meaning that either the employee or the company may terminate the employment relationship at any time, with or without cause or notice, subject to applicable law.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. We will also consider qualified applicants in accordance with the Austin Fair Chance Hiring Ordinance.
If you need assistance or a reasonable accommodation during the application or recruiting process due to a disability, please contact us at [email protected].
Salary Context
This $150K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Elise A.I. Technologies Corp., 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($200K) sits 6% below the category median. Disclosed range: $150K to $250K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Elise A.I. Technologies Corp. AI Hiring
Elise A.I. Technologies Corp. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $250K - $250K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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