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About This Role
Job Code
S1526EO Department Name
Executive Office Reports To
Manager, Data Science FLSA Status
Exempt Union Code
N/A Management
No About Us:
Building Services 32BJ Benefit Funds (“the Funds”) is the umbrella organization responsible for administering Health, Pension, Retirement Savings, Training, and Legal Services benefits to over 185,000 SEIU 32BJ members. Our mission is to make significant contributions to the lives of our members by providing high quality benefits and services. Through our commitment, we embody five core values: Flexibility, Initiative, Respect, Sustainability, and Teamwork (FIRST). By following our core values, employees are open to different and new ways of doing things, take active steps to improve the organization, create an environment of trust and respect, approach their work with the intent of a positive outcome, and work collaboratively with colleagues.
The Funds oversees and manages $11 billion of dollars in assets, which are made up of many, varied and complex funds. The dollars come from a number of sources, including the property owners who pay into the funds on behalf of their employees, and as such, requires those who oversee and manage the money to be highly skilled financial management people.
32BJ Benefit Funds will continue to drive innovation, equity, and technology insights to further help the lives of our hard-working members and their families. We use cutting edge technology such as: M365, Dynamics 365 CRM, Dynamics 365 F&O, Azure, AWS, SQL, Snowflake, QlikView, and more.
Please take a moment to watch our video to learn more about our culture and contributions to our members: youtu.be/hYNdMGLn19A Job Summary:
Under the direction of the Manager, Data Science, we’re looking for an early-career Associate AI Solutions Developer with practical experience in software, data, or automation. In this role, you will use Python and existing AI and data platforms to build, test, and improve applications and workflows, which may include classical Machine Learning and Large Language Models. The work will also involve using AI coding assistants to explore code, draft or refine changes, configure coding agents or subagents, and create tests. You will review and validate AI-generated work and understand the code you submit. This role applies existing AI capabilities; it does not involve training or developing AI models from scratch.
Your responsibilities will include owning agreed assignments from clarification through implementation, testing, and documentation, with support from your supervisor and experienced teammates. Your supervisor will help set priorities, review your approach, and provide coaching through formal weekly check-ins, brief daily discussions to share progress and resolve blockers, and hands-on training. As your skills grow, there will be opportunities to take on broader assignments and greater responsibility.
Depending on the project, you may use Python, Visual Studio Code, PyCharm, GitHub, Databricks and its AI Gateway capabilities, Microsoft Fabric, Microsoft Foundry, and related enterprise data and AI platforms. AI coding assistants may include GitHub Copilot, Claude Code, Genie Code, or Codex. You are not expected to know every tool when you start; the team will provide training and support. Essential Duties and Responsibilities:
- Work with your supervisor and teammates to understand business needs, agree on priorities, and define manageable assignments. Clarify requirements, share progress, and raise questions or blockers.
- Take responsibility for completing agreed assignments through implementation, testing, and documentation, incorporating feedback from your supervisor and teammates.
- Build and improve small applications, agents, and AI-assisted workflows that support clearer member experiences or more efficient internal operations.
- Write and debug Python scripts or application features that process data, automate tasks, or connect approved data sources, APIs, models, and business tools.
- Use approved AI coding assistants to support development tasks, such as understanding a codebase, drafting or refining code, configuring coding agents or subagents, and creating or updating tests. Review and validate suggestions before using them.
- Build and test prototypes and updates to existing solutions. Review your approach with your supervisor and seek guidance and approval for changes affecting system design, security, or production use.
- Test workflows with representative questions and tasks. Record whether results are useful, accurate, consistent, and timely; investigate failures and implement improvements within your assigned scope.
- Use GitHub, Azure DevOps and follow team practices for version control, testing, documentation, and code review. Support experienced teammates as changes are prepared for use and monitored.
- Work with data scientists, data engineers, business partners, IT, and security colleagues to understand data sources, operational needs, and privacy and security requirements.
- Explore relevant tools and capabilities and share practical findings and suggestions with the team. Qualifications (Competencies):
- 1-3 years of professional experience in software development, data, analytics, automation, or a related technical field. Candidates with less professional experience may be considered if internships, fellowships, or substantial projects demonstrate the capabilities below
- Experience writing and debugging Python scripts or small application features that process data, automate tasks, or connect systems. You should be able to explain how the code works, how you investigated problems, and how you verified the results, including contributions developed with AI assistance.
- Experience completing a technical project or assignment that solved a practical problem. You should be able to describe the purpose, your contribution, the challenges you encountered, and how you tested the result.
- Hands-on experience using AI tools in software development through work, an internship, coursework, or a personal project. Examples include using an AI coding assistant to understand, write, refine, or test code. You should be able to explain how you reviewed its suggestions and checked their accuracy.
- Familiarity with Git or another version-control tool for tracking changes and collaborating on code.
- Familiarity with structured data, such as CSV files or JSON, and experience using basic SQL or Python to inspect and organize data.
- Ability to explain technical work clearly, clarify requirements, share progress, incorporate feedback, and recognize when to seek help.
- Willingness to follow established procedures for handling sensitive information.
- Preferred Qualifications: + Experience connecting applications or workflows to APIs, data sources, or business tools. + Exposure to automated testing or collaborative code review. + Experience with Databricks, Microsoft Fabric, Microsoft Foundry, or a similar enterprise data or AI platform. + Experience configuring an AI agent, coding agent, subagent, or workflow. + Experience working with regulated or sensitive data including but not limited to insurance and financial domains. Soft Skills (Interpersonal Skills):
- Curiosity about emerging AI technologies and a preference for testing assumptions through hands-on experimentation rather than theoretical assessment.
- Willingness to report findings objectively, including results that do not support a preferred or previously proposed approach.
- Ability to clearly communicate technical findings, limitations, tradeoffs, and recommendations to technical and non-technical stakeholders.
- Comfort working with ambiguous or loosely defined problems and the ability to translate them into testable questions with measurable success criteria.
- Ability to collaborate effectively across data science, engineering, IT, security, and governance teams, incorporating their requirements early in the development process.
- Attention to detail and follow-through in maintaining versioned, documented, and reproducible work. Education:
Bachelor’s degree in Computer Science, Data Science, Engineering, or a related STEM field, or comparable practical experience through education, internships, projects, or work experience. Reasoning Ability:
High Physical Demands:
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals to perform the essential functions. Work Environment:
The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
Salary Context
This $91K-$107K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% of the market. At Building Service 32BJ Benefit Funds, 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 $212,160 based on 13,809 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $112,320. This role's midpoint ($99K) sits 53% below the category median. Disclosed range: $91K to $107K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Building Service 32BJ Benefit Funds AI Hiring
Building Service 32BJ Benefit Funds has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $107K - $107K.
Location Context
AI roles in New York pay a median of $221,000 across 3,262 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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