Interested in this AI/ML Engineer role at ABM Industries Inc.?
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About This Role
The Director of AI Engineering is responsible for leading the research, design, development, and deployment of AI solutions as per the business strategy. This role bridges AI research with practical engineering, ensuring that AI models are not only innovative but also scalable, efficient, and production ready.
The director will lead and mentor a team of data scientists and AI engineers to build and operationalize AI-driven products and services. This position will collaborate with various stakeholders and cross-functional teams to integrate these custom-built AI solutions into enterprise systems.
Benefit Information:
ABM offers a comprehensive benefits package. For information about ABM’s benefits, visit 2026 Recruiting Flyer - Staff & Mgmt
Pay: $143,500-$205,000
The pay listed is the salary range for this position. Any specific offer will vary based on the successful applicant’s
education, experience, skills, abilities, geographic location, and alignment with market data.
You may be eligible to participate in a Company incentive or bonus program.
Key Responsibilities:
- Collaborate with stakeholders to ensure that technical strategies are in line with ABM’s AI roadmap.
- Conduct comprehensive technical research on emerging AI/ML models and technologies to pinpoint viable solutions for development, MVP creation, and deployment.
- Spearhead MVP development and productization by rapidly prototyping and validating promising AI solutions.
- Align AI initiatives with business objectives by offering expert insights on feasibility, scalability, and integration.
- Oversee the development of machine learning models, including those for NLP, computer vision, and predictive analytics.
- Guarantee that AI models are robust, scalable, and optimized for real-world application.
- Lead research into state-of-the-art AI/ML methodologies for business functions or products, such as deep learning, reinforcement learning, and generative AI.
- Collaborate closely with DevOps to streamline the deployment of AI models into production environments. Establish MLops practice and guardrails
- Implement industry best practices for AI/ML pipelines, including model monitoring, versioning, and periodic retraining.
- Ensure seamless integration of AI models with cloud platforms like Azure (Synapse, ADF, Databricks).
- Establish and enforce ethical AI frameworks to promote fairness, mitigate bias, and maintain transparency in AI systems.
- Work with security teams to protect AI models and data from potential threats.
- Partner with key stakeholders to implement and measure the impact of AI solutions across various business functions.
Required Skills & Expertise:
- Machine Learning & AI: Deep learning, NLP, generative AI, predictive analytics, reinforcement learning.
- MLOps & AI Engineering: Model deployment, cloud-based AI solutions, automation, monitoring, and retraining.
- Cloud & Data Platforms: Azure (Synapse, ADF, Databricks), AWS, Google Cloud, Snowflake.
- Programming & Frameworks: Python, TensorFlow, PyTorch, Hugging Face, Scikit-learn, SQL.
- Expertise and knowledge of OpenAI’s GPT models, Gemini, LLaMA, Alteryx.
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- Big Data & Infrastructure: Spark, Kubernetes, Kafka, data lakes, and scalable AI architectures.
- AI Governance & Compliance: Ethical AI, bias mitigation, data privacy laws (GDPR, CCPA).
Preferred Qualifications:
- Master’s degree in Computer science, Data Science, AI, Machine Learning, or a related field (PhD preferred).
- 12+ years of experience in AI, data science, or ML engineering.
- 5+ years in leadership roles, managing AI and data teams.
- Experience in deploying AI models at scale in enterprise settings.
- Effective communication skills to bridge the gap between AI research, engineering, and business stakeholders.
ABM (NYSE: ABM) is one of the world’s largest providers of integrated facility, engineering, and infrastructure solutions. Every day, our over 100,000 team members deliver essential services that make spaces cleaner, safer, and efficient, enhancing the overall occupant experience.
ABM serves a wide range of market sectors including commercial real estate, aviation, education, mission critical, and manufacturing and distribution. With over $8 billion in annual revenue and a blue-chip client base, ABM delivers innovative technologies and sustainable solutions that enhance facilities and empower clients to achieve their goals. Committed to creating smarter, more connected spaces, ABM is investing in the future to meet evolving challenges and build a healthier, thriving world. ABM: Driving possibility, together.
ABM is an Equal Employment Opportunity (EEO) employer that does not discriminate on the basis of any trait or characteristic protected by applicable federal, state, or local law, including disability and protected veteran status. ABM is committed to working with and providing reasonable accommodation to individuals with disabilities. If you have a disability and need assistance in completing the employment application, please call 888-328-8606. We will provide you with assistance and make a determination on your request for reasonable accommodation on a case-by-case basis.
ABM participates in the U.S. Department of Homeland Security E-Verify program. E-Verify is an internet-based system used to electronically confirm employment eligibility.
ABM is a military-friendly company proudly employing thousands of men and women who have served in the U.S. military. With ABM, you’ll have access to a world-class training program and ample opportunities to use the skills you developed while serving our country. Whether you’re looking for a frontline or professional position, you can find post-military career opportunities across ABM.
ABM directs all applicants to apply at www.abm.com/careers. ABM does not accept unsolicited resumes or submissions outside of this portal. Applicants should submit their application by clicking Apply Now.
For more information, visit www.abm.com
Salary Context
This $143K-$205K range is below 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 ABM Industries Inc., 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($174K) sits 18% below the category median. Disclosed range: $143K to $205K.
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.
ABM Industries Inc. AI Hiring
ABM Industries Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $205K - $205K.
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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