Interested in this AI/ML Engineer role at Bobrick Washroom Equipment?
Apply Now →About This Role
The ideal candidate brings a strong technical foundation in system administration, workflow automation, and API integrations, with demonstrated experience supporting customer service platforms and AI\-enabled tools. Knowledge of Zendesk AI and Dialpad is required to support the broader systems ecosystem this role supports.
KEY RESPONSIBILITIES
Administer, configure, and maintain business systems that support customer operations, internal teams, and cross\-functional workflows.
Design, implement, and optimize automated workflows, integrations, and system logic to improve operational efficiency.
Support AI\-enabled capabilities within CMS, including intelligent routing, agent assistance, and automated triage.
Support system integrations using APIs, middleware, and data connections across platforms (e.g., between Zendesk and our Enterprise Resource Planning (ERP) system.
Monitor system performance, reliability, and data integrity; proactively identify and resolve issues.
Partner with Customer Service Management, Operations, BIT, Business Units, and Sales, to gather requirements and translate them into scalable system solutions.
Maintain system reports, documentation, workflows, and processes to ensure clarity and continuity.
Support system testing, releases, and enhancements while minimizing business disruption.
Ensure systems align with security, compliance, and data governance standards.
Provide tiered system support and troubleshooting for escalated issues.
REQUIRED QUALIFICATIONS
Proven experience in a Systems Administrator, Systems Analyst, or similar technical role, with responsibility for configuring, maintaining, and optimizing enterprise SaaS applications in a production environment.
Strong understanding of system configuration, workflow automation, and platform governance, including the ability to design scalable processes, enforce standards, and manage change control.
Hands\-on experience supporting customer service, case management, or customer engagement platforms, including ticketing, routing, escalation, and self\-service capabilities.
Working knowledge and experience of Zendesk CMS and Zendesk AI (required), including hands\-on experience with AI\-driven ticket routing, agent assistance tools, automation, and intelligent workflows to improve operational efficiency and customer experience.
Experience administering or supporting Dialpad or similar cloud\-based telephony/contact center platforms, including call routing, queue configuration, user provisioning, and platform integrations.
Demonstrated experience with API integrations, data flows, and system interoperability, including working with REST APIs, webhooks, and third\-party integrations to connect customer operations systems with internal tools.
Requires a strong understanding of agentic AI, with the ability to design and manage autonomous, goal\-driven systems that optimize workflows and customer interactions through data\-driven decision\-making.
Ability to analyze end\-to\-end operational processes and translate business requirements into effective technical solutions that align with system capabilities and organizational goals.
Experience supporting or contributing to AI and automation initiatives within customer operations, including intelligent triage, agent co\-pilot tools, self\-service automation, or workflow optimization.
Familiarity with ITSM, CRM, or customer engagement platforms, and an understanding of how these systems support broader operational, service, or customer lifecycle processes.
Strong understanding of reporting, analytics, and performance optimization, including defining metrics, building or supporting dashboards, and using data to drive continuous system and process improvements.
Excellent documentation and communication skills, with the ability to clearly document configurations, workflows, integrations, and operational procedures.
Proven ability to work effectively with cross\-functional teams (BIT, Operations, Quality, and Marketing) in fast\-paced, evolving environments, balancing multiple priorities while maintaining system stability and reliability.
EDUCATION and/or EXPERIENCE
Associate’s degree (A.A.) or equivalent from a two\-year college or technical school, or at least six years of relevant experience supporting enterprise systems, case management systems (Zendesk experience required), technical platforms, or customer operations environments. Equivalent combinations of education, training, and hands\-on experience will also be considered.
LANGUAGE SKILLS
Ability to read, analyze, and interpret general business periodicals, professional journals, technical procedures, or governmental regulations. Ability to write reports, business correspondence, and procedure manuals. Ability to effectively present information and respond to questions from groups of managers, clients, customers, and the general public.
REASONING ABILITY
Ability to solve practical problems and deal with a variety of concrete variables in situations where only limited standardization exists. Ability to interpret a variety of instructions furnished in written, oral, diagram, or schedule form.
PHYSICAL DEMANDS
The physical demands described here are representative of those required by an employee to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to sit. The employee is frequently required to stand; walk; use hands to finger, handle, or feel objects, tools, or controls; reach with hands and arms; and talk or hear. The employee must occasionally lift and/or move up to 35 pounds or more with the aid of material\-handling equipment, such as carts and lifts. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception, and the ability to adjust focus.
WORK ENVIRONMENT
The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. This job will occasionally require travel up to 25% of the time.
The noise level in the office work environment is usually quiet to moderate, but can vary based on travel, site visits, and installation conditions.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 Bobrick Washroom Equipment, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
Bobrick Washroom Equipment AI Hiring
Bobrick Washroom Equipment has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Clifton Park, NY, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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
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