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About This Role
Job ID: 16215
Business Unit: MTA Headquarters
Location: New York, NY, United States
Regular/Temporary: Regular
Department: Corporate IT Products
Date Posted: Jun 17, 2026
Description
JOB TITLE:
Specialist Software Engineer \- AI SALARY RANGE:
$114,070 \- $134,641 DEPT/DIV:Information Technology SUPERVISOR:Product Director Data Science \& Eng LOCATION:
2 Broadway, New York, NY 10004 HOURS OF WORK:9:00 am \- 5:30 pm (7\.5 hours/day) or as required
This position is eligible for teleworking, which is currently 2 days per week. New hires are eligible to apply 30 days after their effective date of hire. Opening:
The Metropolitan Transportation Authority is North America's largest transportation network, serving a population of 15\.3 million people across a 5,000\-square\-mile travel area surrounding New York City, Long Island, southeastern New York State, and Connecticut. The MTA network comprises the nation’s largest bus fleet and more subway and commuter rail cars than all other U.S. transit systems combined. MTA strives to provide a safe and reliable commute, excellent customer service, and rewarding opportunities. Summary:
The MTA transportation network has very large systems and infrastructure for financial, business, automated train, transportation, power, and physical security. The MTA IT Department is centrally responsible for providing a full range of Information and Operational Technology services to the MTA agencies and administrative units through its operating and support units. Services are provided on a 7/24/365 basis in support of the MTA organization and its ridership. MTA IT's Product Development group is empowered, multi\-functional teams focused on the end\-to\-end management of development products from strategy to delivery. Using innovative processes and tools, the teams are responsible for developing and maintaining highly effective, secure, and innovative transportation, operational, and back\-end information systems to support MTA goals and priorities.
This role, as Software AI Engineer, is to design, implement, and maintain innovative AI solutions that solve real\-world problems and drive significant business results. Leverage your expertise in machine learning, deep learning frameworks, and software development to build robust and scalable AI systems. You'll collaborate closely with engineers, data scientists, and business stakeholders to ensure your solutions meet industry best practices and positively impact the company. Contribute software development methods, tools, and techniques, and apply agreed standards and tools to achieve well\-engineered outcomes. Responsibilities:
- Collaborate with cross\-functional teams to understand business needs, define requirements, and translate them into effective AI solutions.
- Develop and maintain machine learning models, ensuring they are accurate, efficient, and scalable.
- Design and implement AI solutions using large datasets, focusing on data security and user privacy.
- Orchestrate the development and maintenance of CI/CD pipelines for deploying and testing AI applications.
- Ensure governance, security, and compliance of all cloud\-based AI solutions
- Implement best practices for AI model deployment, monitoring, and maintenance.
- Write well\-documented, clean, and maintainable code, adhering to best practices and methodologies.
- Continuously iterate and improve upon existing models and solution design/implementation processes, incorporating feedback and lessons learned to enhance accuracy, performance, scalability, and security.
- Stay up\-to\-date with the latest advancements in AI, machine learning, and Azure services, and apply this knowledge to improve our solutions.
- Defines and manages scoping and requirements definition and ensures traceability to source.
- Designs, codes, verifies, tests, documents, amends, and refactors complex programs/scripts and integration software services. Uses appropriate modelling techniques following agreed software design standards, guidelines, patterns, and methodology. Testing: Develops and executes test plans and test cases; implements scalable and reliable automated tests and frameworks.
- Application Support: Maintains application support processes and uses application management software tools to investigate issues, prioritize and diagnose incidents, collect performance statistics, and create reports.
- Project/Product Management: Defines, documents, and executes small projects or sub\-projects. May act as product owner for one or more lower\-value products or services, managing elements of the product life cycle to meet customer/user needs and achieve financial or other targets.
- Quality \& Safety Assurance: Plans, organizes, and conducts quality and safety assessments and suggests opportunities for improvement. Contributes to identifying, analyzing, and documenting hazards and safety risks.
- Supplier Management: Monitors and reports on supplier performance, customer satisfaction, adherence to security requirements, and market intelligence.
- Skills Development: Continuously develops and maintains personal knowledge of software engineering practices, emerging trends, and technologies.
- May mentor less experienced staff
Required Qualifications:
- Bachelor’s Degree in Arts/Sciences (BA/BS)
- Minimum 3 years of relevant experience. An equivalent combination of education and experience may be considered in lieu of a degree.
- Prefer at least one certification in the current platform/domain/technical skill. Possible certifications could be, but are not limited to:
+ Certified Software Engineer or equivalent, Agile Project Management, technical certifications relevant to the specific position (and software products and toolsets used in that position), experience with System Development Life Cycle with the specific methodologies in use in the Department.
+ A software engineering industry certification, including but not limited to: Certified Scrum Developer (CSD), Certified Scrum Master (CSM), Certified Software Development Professional (CSDP), Certified Secure Software Lifecycle Professional (CSSLP), Amazon Certified: AWS Certified Developer, Microsoft Certified: Azure Developer, Certified Software Test Professional (CSTP)
+ Experience administering and developing workflows and specialized UIs in Alfresco or other Document Management systems
Knowledge, Skills and Abilities:
- Hands\-on experience in AI, ML, model development, and deployment in a business setting.
- Strong knowledge of machine learning architectures.
- Strong programming skills in languages such as Python and familiarity with relevant libraries (e.g., TensorFlow, NumPy, PyTorch).
- Experience with deep learning and neural network development.
- Strong statistical foundation, with in\-depth knowledge of supervised, unsupervised, and semi\-supervised machine learning models.
- Good understanding and experience with AI services such as AI Search, Document Intelligence, NLP, and conversational AI.
- Experience working with Generative AI models, with a deep understanding of their capabilities, prompting strategies, model performance, and inherent limitations.
- Experience writing complex, highly optimized SQL queries.
- Solid understanding of software engineering fundamentals. Experience with software development best practices, including version control, automated testing, and continuous integration/continuous deployment (CI/CD).
- Experience building, configuring, and maintaining secure ML cloud infrastructure. Proficiency with Infrastructure as Code technologies such as Terraform.
- Experience with container services, e.g., Kubernetes.
- Good understanding of cloud security best practices.
Preferred Technical Skills:
- Data Structures and Algorithms (Thorough Knowledge/Fully Proficient)
- Database Management (Thorough Knowledge/Fully Proficient)
- Web Development (Thorough Knowledge/Fully Proficient)
- DevOps (Thorough Knowledge/Fully Proficient)
- Operating Systems (Thorough Knowledge/Fully Proficient)
- Cybersecurity, including encryption and authentication (Thorough Knowledge/Fully Proficient)
- Cloud Computing (Thorough Knowledge/Fully Proficient)
- Active Listening, Attention to Detail, Customer Service
- Prioritization, Problem Solving, Effective Verbal and Written Communication
Competencies:
- Collaborates: Building partnerships and working collaboratively with others to meet shared objectives
- Cultivates Innovation: Creating new and better ways for the organization to be successful
- Customer Focus: Building strong customer relationships and delivering customer\-centric solutions
- Values Diversity: Recognizing the value that different perspectives and cultures bring to an organization
- Communicates Effectively: Developing and delivering multi\-mode communications that convey a clear understanding of the unique needs of different audiences
Other Information:
Pursuant to the New York State Public Officers Law \& the MTA Code of Ethics, all employees who hold a policymaking position must file an Annual Statement of Financial Disclosure (FDS) with the NYS Commission on Ethics and Lobbying in Government (the “Commission”).
Equal Employment Opportunity:
MTA and its subsidiary and affiliated agencies are Equal Opportunity Employers, including with respect to veteran status and individuals with disabilities.
The MTA encourages qualified applicants from diverse backgrounds, experiences, and abilities, including military service members, to apply.
Salary Context
This $114K-$134K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $190K across 251 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,133 AI roles we're tracking, AI Software Engineer positions make up 8% of the market. At Metropolitan Transportation Authority, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 863 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($124K) sits 46% below the category median. Disclosed range: $114K to $134K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Metropolitan Transportation Authority AI Hiring
Metropolitan Transportation Authority has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in New York, NY, US. Compensation range: $100K - $134K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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