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About This Role
Milacron is looking for an AI Translator to join our team in Batavia, Ohio. In this position, you will be responsible for serving as the business-facing AI leader for an assigned business unit, translating operational challenges into AI-enabled solutions, driving user adoption, and delivering measurable business outcomes through Milacron’s enterprise AI platform. The AI Translator acts as the primary liaison between the business unit and the Central AI / Data Center of Excellence (CoE), ensuring successful implementation, value realization, and sustainable ownership of AI capabilities.
Work You’ll Do:
- Identify, prioritize, and translate business challenges into structured AI use cases aligned with business unit and enterprise objectives.
- Serve as the primary liaison between business stakeholders and the Central AI / Data CoE to ensure solutions address operational needs and deliver measurable business value.
- Develop business cases, define success metrics, and lead ROI measurement for AI initiatives from concept through deployment.
- Drive AI adoption through stakeholder engagement, training, coaching, communication, and change management activities that embed AI into daily operations.
- Maintain deep expertise in assigned business unit processes, KPIs, systems, and data sources to identify opportunities for optimization, automation, and growth.
- Own accountability for AI-related business outcomes, including productivity improvements, cost reductions, revenue growth, cycle-time reduction, quality enhancements, and working-capital gains.
- Collaborate with AI engineers, data scientists, platform teams, IT organizations, and external partners to define requirements, prioritize initiatives, and validate solution effectiveness.
- Ensure AI solutions comply with governance standards, responsible AI principles, data privacy requirements, and model risk management practices.
- Lead pilot deployments, coordinate cross-functional execution, and facilitate transition of capabilities from external vendors and system integrators to internal business teams.
- Monitor solution performance, user adoption, and business impact, recommending continuous improvements and expansion opportunities.
- Represent the assigned business unit in enterprise AI governance forums and contribute to enterprise-wide AI roadmaps, standards, and best practices.
- Travel domestically and internationally, as required, to support implementation efforts, observe operational workflows, and drive organizational adoption.
- Perform other AI-related activities as assigned by the Senior Director, AI & Engineering or Operating Leadership Team.
Qualifications
- Bachelor’s degree in Business, Engineering, Operations Management, Information Systems, Analytics, Computer Science, or a related field.
- 7+ years of progressive experience in manufacturing, industrial operations, supply chain, commercial operations, engineering, finance, or other business leadership functions.
- Demonstrated success driving measurable business outcomes through process improvement, digital transformation, operational excellence, or technology-enabled initiatives.
- Proven experience owning KPIs tied to revenue, margin, cost reduction, productivity, service levels, or working-capital performance.
- Strong understanding of AI, machine learning, generative AI, automation technologies, and modern data platforms sufficient to evaluate opportunities and engage effectively with technical teams.
- Experience leading cross-functional projects involving business and technical stakeholders.
- Proven change management and organizational adoption experience with a focus on influencing behavior and driving sustained business results.
- Excellent communication, facilitation, and presentation skills with the ability to translate complex technical concepts into practical business decisions.
- Proficiency with Microsoft 365, Power BI, Salesforce (SFDC), and other enterprise business systems.
- Ability to work effectively in a global, matrixed organization and influence stakeholders without direct authority.
- Master’s degree in Business Administration (MBA), Engineering, Analytics, Data Science, or a related discipline preferred.
- Experience implementing AI, advanced analytics, automation, or digital transformation initiatives within industrial or manufacturing environments preferred.
- Experience working alongside system integrators, consulting firms, or technology partners and successfully transitioning solutions to internal ownership preferred.
- Plastics processing, capital equipment manufacturing, aftermarket service, or industrial machinery industry experience preferred.
- Experience with Palantir Foundry, Ontology, or comparable enterprise AI platforms preferred.
- Training or certification in Change Management, Lean, Six Sigma, Project Management, or related disciplines preferred.
- Experience supporting responsible AI governance, data management, or enterprise technology strategy initiatives preferred.
- ML engineering, data science, or PhD-level technical expertise is not required and does not substitute for business operating experience.
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Who we are:
Milacron is a global industrial leader in the manufacturing and distribution of highly engineered and customized injection molding and extrusion solutions, and aftermarket parts and service for the plastics processing industry. The company’s 1,900+ associates worldwide collaborate to serve a diverse range of end-markets, such as automotive, construction, medical, telecommunications, material handling, packaging, and much more. With expertise in plastics spanning nearly 60 years, Milacron holds longstanding relationships with more than 27,000 customers around the world. The original equipment provider of plastics manufacturing solutions is the largest US-owned and globally served of its kind. Headquartered in Batavia, OH, Milacron’s global footprint is also serviced through regional facilities in Asia, Europe, and South America.
EEO:
The policy of Milacron is to extend opportunities to qualified applicants and employees on an equal basis regardless of an individual's age, race, color, sex, religion, national origin, disability, sexual orientation, gender identity/expression or veteran status. We are committed to being an Equal Employment Opportunity (EEO) Employer and offer opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us at [email protected]. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Milacron, 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 $212,000 based on 11,866 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 $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Milacron AI Hiring
Milacron has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Batavia, OH, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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