Interested in this AI/ML Engineer role at Eaton?
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What you’ll do:
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If you’re excited about building platforms that power the next wave of intelligent infrastructure and want to influence how AI transforms the data center ecosystem, the Director of Product Management, Developer Platforms role will give you the scale, complexity and impact to do it.
About the role:
We’re looking for a bold, technically fluent Director of Product Management, Developer Platforms to shape the future of AI\-driven software in the data center and distributed infrastructure space. In this role, you will lead the evolution of our Brightlayer AI developer ecosystem —building analytics capabilities and SDKs that power next\-generation applications across modern data centers, edge environments, and hybrid multi\-cloud architectures.This is a highly visible role that sits at the intersection of AI innovation, software platforms, and connected hardware. You will shape how developers and customers interact with our ecosystem—unlocking new use cases, accelerating time\-to\-value, and strengthening our competitive position.This is not a traditional product role. You’ll be at the forefront of where software meets physical infrastructure, enabling developers to unlock intelligence from mission\-critical systems through scalable, secure, and high\-performance digital platforms.
What you will drive:
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- Define the vision for Brightlayer’s developer platform—creating SDKs, APIs, and analytics services that developers rely on to build data\-driven applications in complex infrastructure environments
- Accelerate developer adoption by delivering world\-class, open, and extensible tools aligned with modern open\-source and cloud\-native practices
- Deliver real\-time , actionable analytics across data center, on\-prem, edge, and multi\-cloud systems—turning operational data into intelligence
- Partner across engineering, platform architecture, AI/data science , and UX to bring high\-performance, production\-grade capabilities to life
- Engage directly with customers and hyperscale/data center operators, understanding their challenges and ensuring our platform solves real\-world problems at scale
- Push the boundaries of AI \+ infrastructure convergence, integrating advanced analytics with connected hardware systems to create differentiated, end\-to\-end solutions
What you will bring:
- Platform SDK ownership: Define and manage the SDK roadmap aligned with our platform strategy, ensuring seamless integration with APIs, telemetry systems, and orchestration tools to translate platform capabilities into clear business outcomes.
- Analytics enablement: Collaborate with data engineering and analytics teams to expose platform metrics, logs, and events through SDKs, enabling internal teams and external partner/customer developers to build custom dashboards, alerts, analytics tracking and automation.
- Cross\-functional leadership: Partner with engineering, product management, and customer success teams to prioritize features, resolve technical challenges, and deliver high\-impact releases.
- Customer engagement: Act as the voice of the developer, gathering feedback from enterprise customers, partners, and internal stakeholders to inform product decisions through informed value propositions and use cases.
- Security \& compliance : Work with security and compliance teams to ensure SDKs meet enterprise\-grade standards for authentication, authorization, and data handling.
- Committee leader : Represent Eaton in open\-source projects that are key to the platform, such as OCP sub\-committees for RedFish.
- Lifecycle management: Own versioning, release planning, and backward compatibility strategies for SDKs across multiple platforms.
- Market awareness: Stay informed on trends in data center automation, observability, and developer tooling to maintain competitive advantage.
Qualifications \& Additional Information:
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- Bachelor’s degree in computer science, engineering, or related field; advanced degree or certifications in cloud/data center technologies is a plus.
- Minimum of seven (7\) years of product management experience, with a focus on SDKs, APIs, or developer platforms in the data center or cloud infrastructure space.
- Minimum of seven (7\) years of experience working with cross\-functional teams in Agile environments
- Minimum of five (5\) years of experience with developer platforms in the data center or cloud infrastructure space
- Minimum of five (5\) years of experience with analytics platforms (e.g., Splunk, ELK, Grafana, Prometheus) and telemetry pipelines.
- Minimum of five (5\) years of experience using DevOps tools and practices (e.g., Terraform, Ansible, CI/CD).
- This role may be based at our offices in the following locations: Beachwood, OH; Menomonee Falls, WI; Moon Township, PA; Raleigh, NC.
- Relocation will be offered within the hiring country.
- Travel for this position will require up to 25% (domestic and international)
- Eaton will not consider applicants for employment immigration sponsorship or support for this position. This means that Eaton will not support any CPT, OPT, or STEM OPT plans, F\-1 to H\-1B, H\-1B cap registration, O\-1, E\-3, TN status, I\-485 job portability, etc.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Eaton, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Eaton AI Hiring
Eaton has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Raleigh, NC, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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