Interested in this AI/ML Engineer role at Watlow?
Apply Now →Skills & Technologies
About This Role
Working at Watlow
Watlow is a global technology and manufacturing leader who provides world class engineering expertise through innovative thermal products and systems, enabling our customers to thrive. We are making a positive impact every day as our solutions enrich the lives of people everywhere. We have been providing breakthrough thermal solutions for over 100 years! Our corporate values guide us uncompromisingly to always do the right thing, continually learn and improve, respect everyone, and lead with service and humility.
At Watlow's global headquarters in St. Louis, Missouri, our IT team partners with business leaders across the company to improve how Watlow works around the world. Part of a global thermal solutions leader founded in 1922, our heaters, sensors, and controls power industries like semiconductor, aerospace, energy, and industrial equipment.
#PoweredByPossibility
We are hiring an: AI Strategy & Transformation Manager
St. Louis, Missouri
Watlow is seeking an AI Strategy & Transformation Manager who will help shape how Watlow puts AI to work across the enterprise. This is a newly created, highly consultative, individual contributor role where your ability to ask the right questions, influence senior leaders, and prioritize opportunities matters just as much as your understanding of AI. AI is already widely used at Watlow; this individual will bring structure to that use and make sure it delivers measurable value for the business. They will partner with executive and functional leaders to understand their goals, turn them into practical AI initiatives, and drive adoption across manufacturing operations, procurement, quality, engineering, sales, and other functions.
The scope is enterprise-wide and global, spanning the US, Mexico, Europe, and Asia. They will work alongside internal technical teams and global delivery centers, and will report to the Vice President of IT. The ideal candidate brings transformation or consulting experience, hands-on familiarity with LLMs and RAG-based solutions, and the credibility to advise executives with both vision and realism. This role includes periodic domestic and international travel, including Mexico, Europe, and Asia.
What you'll do:
- Executive partnership: Partner with Watlow's leadership teams to understand their business priorities and identify high-value opportunities for AI.
- Opportunity assessment: Assess business processes, products, and operations to uncover ways AI can lower operating and manufacturing costs, improve efficiency, and support growth, with an initial focus on operations.
- Roadmaps and business cases: Build AI adoption roadmaps, develop business cases, and help leaders prioritize initiatives based on value and feasibility.
- Discovery and facilitation: Lead discovery workshops with stakeholders to define problems, explore use cases, and align on next steps.
- Solution translation: Translate business challenges into clear requirements and work with internal technical teams to turn them into practical solutions.
- Delivery prioritization: Set priorities for Watlow's global delivery centers so the right projects move forward, without direct management responsibility for those teams.
- Enterprise AI integration: Help teams get more value from the AI tools Watlow already has by building them into everyday work, rather than adding new tools.
- Adoption and change management: Drive training, use case development, and change management so AI tools are adopted and used effectively across functions.
- Grounded advisory: Give leaders honest, practical feedback on what it will take to bring their vision to life, including risks, barriers, and realistic timelines.
- Best practice sharing: Bring industry best practices into Watlow and share learnings across regions and business units.
What you'll need to succeed?
- Bachelor's degree in business, engineering, information systems, or a related field
- 7+ years of experience in digital or business transformation, technology consulting, product strategy, or a related role
- Proven experience partnering with and influencing executive and senior stakeholders
- Hands-on experience applying large language models (LLMs) such as ChatGPT, Claude, or Copilot to business problems
- Experience designing or delivering retrieval-augmented generation (RAG) or enterprise knowledge solutions
- Strong communication and presentation skills, from the shop floor to the executive level
- Willingness to travel up to 20% domestically and internationally
- Authorization to work in the United States without current or future sponsorship
Preferred qualifications:
- Big 4 or technology consulting experience, especially in a digital, AI, or transformation practice
- Experience in discrete manufacturing; continuous manufacturing such as oil and gas is also considered
- Experience with sales transformation, including opportunity management and opportunity to project to execution processes
- Familiarity with product taxonomy or product data management
- Exposure to engineering or new product development processes
- Experience applying AI or process improvement in procurement, quality, or manufacturing operations
- Experience working in global, multi-region organizations
- Experience working with offshore or global delivery teams
- MBA or other advanced degree
Benefits: The Watlow Total Compensation Plan
The health, well-being and financial stability of you and your family is a high priority to us.The Watlow Total Rewards Plan includes competitive compensation and a full range of life and career enhancing benefits:
- Annual Incentive Program
- 401(k) plan that includes a company match on your contribution and an annual company contribution that is tied to company performance
- Wellness incentives
- Employee Personal Assistance Program
- Dental, medical, vision and short-term and long-term disability insurance
- Paid holidays, personal time, and vacation
- Parental leave
- Tuition reimbursement
Diversity & Inclusion
We proactively embrace diversity in all its dimensions across our company and cultivate a culture of inclusion and forward thinking that respects and reflects each team member’s individual strengths, views, and experiences. Watlow takes pride in being an inclusive equal opportunity employer and considers for employment qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
Please let us know confidentially if you need or require any special accommodations to participate in our recruiting process by emailing 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Watlow, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Watlow AI Hiring
Watlow has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in St. Louis, MO, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.