Interested in this AI/ML Engineer role at Benchmark?
Apply Now →About This Role
At Benchmark , we are driven by our purpose: to innovate for a healthier, safer, and better-connected world to create a brighter future. When you join us, you become part of a team passionate about making a meaningful impact across various sectors, including commercial aerospace, defense, advanced computing, next-generation communications, complex industrials, medical, and semiconductor capital equipment. We prioritize career growth, fostering a culture that ensures you reach your full potential. At Benchmark, your work is purpose-driven, enabling you to create a positive impact on the world and make your career truly meaningful. You’ll be part of a collaborative and inclusive team that values your ideas and contributions.
Summary:
Provides strategic and operational leadership for the company’s artificial intelligence initiatives. This individual creates the enterprise AI strategy, leads a cross-functional AI Council to assess and prioritize projects, researches and determines future AI investments, and ensures effective management of AI budget allocations. The role balances the growth of home-grown AI tools with the company’s portfolio of licensed third-party solutions to save costs and optimize AI performance for company-specific applications, while setting and maintaining a healthy AI culture across the organization.
Responsibilities:
AI Strategy, Governance & Portfolio Leadership
- Develops, owns, and maintains the company’s enterprise AI strategy and roadmap, aligned to corporate objectives and the priorities of the Chief Technology Officer and the AI Steering Committee.
- Leads a cross-functional AI Council to intake, assess, and prioritize AI projects spanning manufacturing operations, trade and compliance, human resources, legal contract review, engineering design, sales and reporting, supply chain analysis, and other functions.
- Researches emerging AI practices, models, and platforms to determine future AI investments, balancing commercial third-party solutions with home-grown systems that reduce cost and optimize performance for company-specific applications.
AI Execution, Budget & Value Realization
- Manages the AI budget and capital allocations, providing forecasts, business cases, and return-on-investment analyses to leadership for prioritized initiatives.
- Directs the design, development, and deployment of home-grown AI tools and systems, and defines when to build versus buy across the portfolio of licensed third-party AI solutions.
- Defines success metrics, tracks value realization, and reports portfolio status, risks, and outcomes to the Chief Technology Officer and executive stakeholders on a regular basis.
Responsible AI, Security & Adoption
- Establishes AI governance, data privacy, security, and responsible-AI standards in partnership with IT, Legal, and Cybersecurity, including model risk management and compliance controls.
- Sets and maintains a healthy, ethical AI culture across the company, championing adoption, upskilling, and change management for tools such as Microsoft Copilot and GitHub Copilot.
- Ensures released Benchmark process instructions and applicable regulatory requirements are followed in the development and operation of AI systems.
Leadership & Team Development* Partners closely with leaders in Operations, Supply Chain, Finance, Legal, Human Resources, IT, and Engineering, and with international manufacturing operations leaders, to translate business needs into prioritized AI solutions.
- Coordinates with regional Directors of Engineering in the United States, the Netherlands, and Malaysia, and with the VP of Program Management, to align AI initiatives with regional and program roadmaps.
- Represents the company’s AI capabilities in support of customer engagements, technical exchanges, and sales activities as needed.
- Builds, mentors, and grows a team of AI engineers and data scientists, including guidance for career path planning, technical mentoring, and performance appraisals.
- Recommends and selects external partners as needed.
Knowledge, Skills & Abilities
- Bachelor of Science degree, preferably in computer science, data science, electrical engineering, or related studies; advanced degree preferred.
- Must have 8+ years of experience in software, data, or AI/ML environments, with 3+ years in a leadership role.
- Demonstrated experience leading cross-functional technology initiatives and managing budgets in a global organization preferred.
- Working knowledge of modern AI/ML methods, LLM’s, and platforms such as Microsoft Copilot and GitHub Copilot.
- Intermediate knowledge of cloud, data, and MLOps practices for building and operating home-grown AI systems.
- Capability to teach complex AI concepts and procedures in an easy-to-understand method.
- Effective communication skills, both written and verbal, with ability to write to technical and executive audiences
- Ability to assemble and present technical presentations in support of technology exchange and Benchmark sales activities.
- Strong management skills, leading a diverse, geographically distributed team of individuals.
WORK ENVIRONMENT
- Must be able to lift, carry, push, or pull up to 5 pounds 5% or less of the workday.
- Must be able stoop, kneel, crouch, or crawl 5% or less of the workday.
- Must be able to talk, see, hear, concentrate, think, and reason.
- Must be able to sit for prolonged periods of time throughout the workday.
- Must be able to use a keyboard and do manual tasks for prolonged periods of time throughout the workday.
- Office work environment with hybrid flexibility.
- Will be able to travel up to 10% - 20% of the time, including international, day and/or overnight travel.
Education: Bachelor of Science degree, preferably in computer science, data science, electrical engineering, or related studies; advanced degree preferred.
Geographical Location: Arizona:Tempe
Physical Location: Tempe, AZ - Headquarters
Shift: Shift 1
Work Schedule: M-F 08:00am-5:00pm
Full Time
In our innovative environment, you'll work with cutting-edge technology, leading the way in engineering design and delivering world-class manufacturing services for products that make a real difference in the lives of others. At Benchmark, your career is more than just a job. It’s an opportunity to be part of something bigger, where your work matters and helps shape a brighter future for everyone. Together, we will achieve extraordinary things.
Export Control: This job position may include access to controlled information or technology covered under applicable U.S. export control laws. As such, employment for this job position may be contingent on either verification that an applicant falls under the definition of a “U.S. Person” (which includes U.S. citizens, U.S. lawful permanent residents, and those granted U.S. asylum or refugee status) or on the Company timely obtaining any necessary export license required under federal laws. The Company evaluates such export license situations on a case-by-case basis and may decline to proceed with a job applicant in its sole discretion since export license applications can take many weeks to be processed.
Benchmark is an equal opportunity employer. We are bringing together a dynamic workforce with unique talents, life experiences, cultures, and perspectives to promote an innovative, collaborative, and creative place to work. If you need assistance or an accommodation due to a disability, please email us at [email protected] .
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Benchmark, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Benchmark AI Hiring
Benchmark has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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