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About This Role
Job Summary:
Responsible for bridging Milacron's business operations and the enterprise AI platform within an assigned business unit (Supply Chain, HR, Finance, Operations, Engineering, Sales, Aftermarket, or India). The AI Translator is a credible business operator — not a machine learning engineer — who translates business problems into AI use cases, drives model adoption on the ground, owns the EBITDA outcomes associated with the BU's AI initiatives, and absorbs capability from external system integrators so the business can run independently. This role is the connective tissue between the Central AI / Data Center of Excellence and the business unit it serves, and is expected to deliver from problem identification to first pilot within 60–90 days under Milacron's embedded hybrid AI operating model.
Essential / Key Functions:
- Translate business problems into structured AI use cases for the Central AI / Data CoE, and translate model outputs, assumptions, and constraints back into business language for the BU leadership team
- Maintain deep BU\-specific fluency in workflows, KPIs, systems of record, and the underlying data sources owned by the assigned business unit
- Own change management on the ground — train, coach, and earn user trust so AI adoption sticks beyond pilot and becomes the default way of working
- Hold accountability for the EBITDA KPIs assigned to the BU's AI portfolio (e.g., revenue uplift, cost reduction, cycle\-time improvement, working\-capital release)
- Govern AI deployments within the BU by flagging model risk, data quality issues, biased or unintended outputs, and escalating appropriately to the CoE governance function
- Absorb capability from system integrators and external vendors so the BU can sustain, extend, and eventually evolve AI solutions without ongoing third\-party dependency
- Partner with Central AI / Data CoE platform engineers and ML developers to prioritize the backlog, define data contracts, and validate model performance against business outcomes
- Lead use\-case discovery, business case development, and post\-deployment ROI measurement for AI initiatives within the BU, in alignment with enterprise investment guardrails
- Coordinate data preparation, system integration, and pilot rollouts with internal IT, data engineering, and external delivery partners
- Champion responsible AI practices including data privacy, model explainability, human\-in\-the\-loop controls, and clear audit trails for AI\-influenced decisions
- Represent the BU in enterprise AI governance forums and contribute to the company\-wide AI roadmap and investment prioritization
- Travel domestically and occasionally internationally to BU sites, customer facilities, and partner / vendor locations as required to observe workflows and drive adoption
- Other AI\-related activities as requested by the Senior Director, AI \& Engineering or the Operating Leadership Team
Minimum (Required) Qualifications:
- Bachelor's degree in business, engineering, operations, analytics, computer science, or a related discipline; advanced degree preferred but not required
- 7\+ years of progressive experience in operations, business management, or functional leadership within a manufacturing, industrial, or capital equipment environment
- Demonstrated track record of owning and delivering measurable P\&L outcomes (revenue, cost, margin, working capital, or service\-level performance)
- Working fluency with AI / ML concepts, generative AI applications, and modern data platforms — sufficient to scope use cases, evaluate vendor proposals, and challenge model assumptions, without requiring hands\-on model development
- Strong stakeholder management skills, with proven ability to influence senior business leaders, technical teams, and external partners across multiple time zones
- Experience leading change management and digital adoption programs in environments with mixed levels of technical comfort and digital maturity
- Proficient with Microsoft 365, Salesforce (SFDC), PowerBI or equivalent BI tools, and modern collaboration platforms
- Excellent written and verbal communication skills, with the ability to move fluidly between technical and non\-technical audiences and between front\-line operators and executive leadership
- Sound judgment on data quality, model risk, and the limits of AI — and the willingness to push back on use cases that do not meet the bar for business value or responsible deployment
- Prior experience working alongside system integrators or consulting partners and successfully transitioning capability in\-house is strongly preferred
- Plastics processing, capital equipment, aftermarket service, or industrial manufacturing domain knowledge is strongly preferred
- An ML engineering, data science, or PhD background is not required and is not a substitute for business operating experience
- Prior experience with Palantir Foundry and/or Ontology is strongly preferred.
Physical Demands \& Work Environment:
Physical and sensory demands are representative of an employee working in fast\-paced office, manufacturing plant, and customer\-facility environments. While performing the duties of this job, the employee is regularly required to read, communicate, write, type, and sit for extended periods, and to move through plant floor and customer\-facility environments to observe workflows and engage with operators.
Employee may be periodically exposed to moving equipment, mechanical parts in motion, and other hazards associated with a manufacturing environment such as overhead cranes, welding, machining, and painting. Domestic and occasional international travel to BU sites, customer facilities, and partner / vendor locations is expected.
Disclaimer:
This job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice.
Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. The ADA defines “reasonable accommodation” as a change or adjustment to a job or work environment that allows a qualified individual with a disability to satisfactorily perform the essential functions of a particular job and does not cause an undue hardship for the employer. Examples of reasonable accommodations may include:
- Additional modifying equipment or devices;
- Modified work schedules;
- Providing an interpreter; or
- Making the work environment readily accessible to individuals with disabilities.
\#LI\-DM1 \#LI\-ONSITE
Who we are:
Milacron is a global leader in the manufacture, distribution and service of highly engineered and customized systems within the $27 billion plastic technology and processing industry. We are the only global company with a full\-line product portfolio that includes hot runner systems, injection molding, extrusion equipment. We maintain strong market positions across these products, as well as leading positions in process control systems, mold bases and components, maintenance, repair and operating (“MRO”) supplies for plastic processing equipment. Our strategy is to deliver highly customized equipment, components and service to our customers throughout the lifecycle of their plastic processing technology systems.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
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, 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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