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About This Role
About MISUMI Americas
MISUMI Americas, a division of MISUMI Group, is a leading provider of standard, configurable, and custom manufacturing solutions. By integrating a vast catalog of components with a world\-class digital manufacturing platform, MISUMI Americas empowers engineers and procurement teams to accelerate innovation across the entire product lifecycle. With operations in the San Francisco Bay Area and Chicago, the company serves as a vital partner for the most innovative companies in the Americas.
Director of Enterprise AI — MISUMI Americas
Oakland, CA \| Full\-time \| Director\-level \| $220,000–$260,000 DOE \| \~10% travel
The role
As the Director of Enterprise AI, you will own the AI strategy and adoption across MISUMI Americas. This is a highly visible role at the center of our mission: to build the next\-generation operating system for New Product Development. We use artificial intelligence, automation, and deep manufacturing intelligence to change how products are designed, sourced, and delivered globally. You will be responsible for the AI "power tools" that make us faster and more competitive across all business functions, at enterprise scale.
You will drive how AI opportunities are discovered, evaluated, and prioritized across the company. You will build and lead a team to assist you in coaching champions and accelerating adoption. And you will work with architecture and product to set the governance standards that move initiatives from prototype to production. AI is an organizational priority at MISUMI, which means leadership is actively engaged and business teams are open to experimentation.
We want to create a company where people can do the best work of their lives. AI is part of how we will get there: by giving people better tools, automating routine work, and making room for more creative and strategic contributions across the organization.
This role is based in our Oakland, CA office.
What you'll own
- Own MISUMI Americas' enterprise AI portfolio: the tools, workflow automations, and agents that serve every function across the company
- Define the strategy and framework for how AI use cases are discovered, evaluated, and prioritized across the enterprise
- Measure the impact of AI initiatives by business outcomes (hours saved, risk reduced, revenue generated, etc.), not by usage metrics or tokens consumed
- Work with architecture and product leaders to establish governance standards for AI initiatives, such as when a prototype moves onto the product roadmap, and what tools business users can own versus what requires core platform development
- Build a structured AI champion network by identifying high\-potential people in each function, developing their capabilities, and using them to surface new use cases and proof\-of\-concepts
- Design onboarding and enablement programs that get teams productive quickly, including prompt libraries, workflow guides, office hours, and self\-serve resources
- Embed with business teams to understand their actual workflows on the ground and where AI can help
- Hire and lead a small team focused on coaching champions, enabling adoption, and helping business teams get value from AI tools
- Communicate AI strategy, progress, and business impact to executive leadership. Make the case for what to invest in, what to hold, and what to stop funding.
- Partner with architects to shape MISUMI Americas' AI vendor and platform strategy: what LLM provider we standardize on, what automation tools we buy vs build ourselves, and how we stay flexible as better tools come to market
What we're looking for
- 12\+ years of experience leading an enterprise through technology and workflow transformations
- Proven success designing and running enablement or change management programs — training rollouts, adoption campaigns, internal champion networks
- Experience operating within technology governance frameworks, with the ability to build a business case that meets ROI, security, and architectural standards
- Firsthand, regular use of enterprise AI tools like Claude, Copilot, or similar platforms — leadership is actively experimenting and expects you to be ahead of them
- Track record of making hard prioritization calls with an executive audience while encouraging curiosity and experimentation in the teams closest to the work
- Full product lifecycle ownership on ambiguous business problems — discovery through measurement and iteration
- Ability to measure and report AI/automation impact in dollars
- Technical fluency sufficient to evaluate vendor claims, push back on engineering estimates, and earn the respect of senior engineers — you don't need to build things, but you need to understand what's being built
- Manufacturing, supply chain, or industrial operations experience is a big plus
Compensation \& location
$220,000–$260,000 base salary, depending on experience.
Work Authorization: Must be a U.S. citizen or U.S. permanent resident (Green Card holder)
Based in Oakland, CA, with approximately 10% travel for leadership meetings and building face\-to\-face relationships across MISUMI Americas.
We're actively seeking teammates who:
- Bring diverse perspectives and experience to our culture and company.
- Excel at being part of a strong, empathetic team.
- Thrive in an environment emphasizing respect, honesty, collaboration, and growth.
- Have an 'always learning' mindset that celebrates learning, not just wins.
- Help us continue to build a world\-class organization that values the contributions of all of our teammates
We encourage applications from members of underrepresented groups, including but not limited to women, members of the LGBTQ community, people of color, people with disabilities, and veterans.
Salary Context
This $220K-$260K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Fictiv, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($240K) sits 10% above the category median. Disclosed range: $220K to $260K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Fictiv AI Hiring
Fictiv has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Oakland, CA, US. Compensation range: $230K - $260K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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