Interested in this AI/ML Engineer role at HR&A Advisors?
Apply Now →Skills & Technologies
About This Role
ABOUT US \| HR\&A Advisors, Inc. (HR\&A) is an employee\-owned company advising public, private, non\-profit, and philanthropic clients on how to increase opportunity and advance quality of life in cities.
We believe in creating vital places, building equitable and resilient communities, and improving people’s lives.
From Brooklyn to London, Medellin to Hong Kong, we have guided hundreds of clients in transforming real estate and economic development concepts, and public infrastructure, first into actionable plans and then into job\-producing, community\-strengthening assets.
Our clients include real estate owners and investors, hospitals and universities, cultural institutions and philanthropies, community development organizations, and governments.
HR\&A has offices in Atlanta, Dallas, Los Angeles, New York, Raleigh, Washington D.C. and the Bay Area. We come from diverse backgrounds, have a breadth of lived experience, and share a passion for cities. We are former city officials, executive directors, planners, lawyers, architects, and economists.
Hear more about the HR\&A experience from our staff.
Learn more about careers at HR\&A on our website here.
THE OPPORTUNITY \| HR\&A Labs is a rapidly growing internal team that works closely with project teams across all of our business lines to prototype products, serve data science requests, and increase the quality and efficiency of our work. Over the past four years, HR\&A Labs has developed dashboards, products, and actionable data analysis to state and local governments to push forward housing access, broadband adoption, and data transparency.
This position will primarily support the design and implementation of internal data tools and be deployed onto projects to help provide data science and analytics expertise across a wide set of business lines.
THE ROLE \| HR\&A Labs is seeking a Data Science and Analytics Specialist to support the rapid growth of our digital services offerings. This role is based in our New York office. This role is for early\-career professionals with roughly 2\-4 years of experience who want to grow into owning the substance of client and internal deliverables. As a Data Science and Analytics Specialist, you will contribute to and increasingly drive complex data analytics, visualization, and web development work, with high impact across numerous projects and the opportunity to build both our clients' capacities and your own skills quickly.
EXPERIENCE REQUIRED \| You will support the rapid growth of our digital service offerings by conducting data and geospatial analysis, developing visualizations, building interactive tools and dashboards, and developing methodologies for key economic indicators. Day\-to\-day work includes procuring and analyzing large datasets, building and maintaining data systems and tools, and contributing to fast\-moving project teams. You'll start by driving discrete pieces of the work and grow into leading workstreams as you demonstrate ownership.
- 2\-4 years of relevant professional experience, or a strong portfolio that demonstrates equivalent capability in analytics and product thinking
- Proficiency in Python, including geospatial analysis and packages (e.g., GeoPandas, Shapely)
- Demonstrated ability to work with census data, PUMS, and other economic and demographic datasets
- Proficiency cleaning, manipulating, and merging public, proprietary, and internal datasets for analysis
- Fluency with LLMs and other AI tools as part of a daily workflow, paired with the judgment to independently verify data and AI outputs and catch when they are wrong
- Clear written and verbal communication, including the ability to translate technical analysis into something a non\-technical client can read and act on
- Evidence of self\-directed drive: something you built, shipped, or contributed to that nobody assigned you (a side project, a tool, an open\-source contribution)
- Ability to manage your own deadlines across multiple workstreams and escalate early when something is at risk
- Bachelor’s degree (master’s preferred) with a focus on data science/analytics, geographic information science/analytics, urban planning/design/data, regional science/analytics, architecture, economics, business, public policy, real estate, or other related fields
Preferred:
- Experience building web\-based tools and dashboards using publicly available APIs
- Experience with front\-end development (React/Next.js) and/or back\-end services (FastAPI)
- Experience with spatial data infrastructure (PostGIS, vector tiles) or analytical databases (DuckDB)
- Experience conducting geospatial, vicinity/buffer, site suitability, socio\-economic, and demographic analysis
- Experience in product design, rapid prototyping (including with generative AI tools), and wireframing
HYBRID WORK POLICY \| HR\&A fosters a collaborative and flexible work environment through our hybrid work policy. Employees work from the office at least three days a week, which allows individuals the freedom to balance their professional and personal lives while maintaining a strong connection to their teams.
COMPENSATION \| The base salary range for this position is $83,200 to $98,571, plus the opportunity for a discretionary year\-end bonus. Where an offer falls inside this pay range is dependent on experience. We offer competitive compensation packages, based on qualifications and experience. We are an employee\-owned company, meaning you will have the opportunity to benefit from the firm’s growth over time through participation in our Employee Stock Ownership Plan. Each year, the firm will contribute funds to this long\-term wealth\-building account and may make contributions to other retirement accounts. We also provide a comprehensive benefits package that goes well beyond coverage of 90\-95% of healthcare premiums, including dental and vision coverage.
HOW TO APPLY \| HR\&A is committed to attracting and retaining a talented, diverse, competitive team of professionals dedicated to solving the challenges of urban life. Women, people of color, members of the LGBTQ community, individuals with disabilities, and veterans are strongly encouraged to apply.
To apply, click here. Please submit your cover letter and resume as a single PDF document. We ask that you submit a version of your resume that has your school information removed. There is no need to reformat your resume, and you should leave your degree (e.g., “B.A. Communications”). However, please remove all undergraduate and graduate school name references. This request is part of our ongoing work to build a hiring system that is free from bias and based on candidate merit and performance in the hiring process.
All qualified candidates will receive consideration for employment without regard to their race, religion, ancestry, national origin, sex, sexual orientation, gender identity or expression, age, disability, marital status, medical condition, veteran status, or any other basis as protected by federal, state, or local law.
For more information, please contact us at [email protected].
TsAhPSZWRW
Salary Context
This $83K-$98K range is in the lower quartile 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 HR&A Advisors, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($90K) sits 58% below the category median. Disclosed range: $83K to $98K.
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.
HR&A Advisors AI Hiring
HR&A Advisors has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $98K - $98K.
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.