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About This Role
Position Purpose:
An AI Engineer will design, develop, and deploy intelligent solutions that optimize manufacturing processes, improve operational efficiency, and enable data\-driven decision\-making. This role also supports manufacturing, engineering and quality functions, including development of analytical models and participation in PPAP (Production Part Approval Process) documentation and validation.
Essential Functions:
- 1. AI \& Machine Learning Development
- Design, develop, and deploy machine learning models for manufacturing use cases
- Build an automated quoting tool, pulling data from material indexes, routing times, and historical similar parts to reduce quoting time.
- Build predictive models for maintenance, quality control, and demand forecasting
- Develop computer vision systems for defect detection and inspection
- Create and maintain statistical and analytical models supporting engineering and quality validation
- Optimize algorithms for real\-time industrial applications
- Engineering \& Quality Support
- Develop AI/ML models to support process capability analysis, root cause analysis, and quality improvement initiatives
- Develop AI design and simulation tools for engineering
- Partner with engineering teams to model process parameters, tolerances, and product performance
- Support APQP and PPAP activities, including:
- + Contributing data analysis for Process Capability
+ Supporting Measurement System Analysis (MSA)
+ Assisting with control plans and validation data packages
+ Ensuring AI models align with customer and regulatory quality requirements
- Provide data\-driven insights for continuous improvement and defect reduction
- Data Engineering \& Processing
- Collect, clean, and preprocess data from machines, sensors, and IoT devices
- Integrate data pipelines from ERP, MES, and SCADA systems
- Ensure data quality and traceability for quality audits and PPAP documentation
- Manufacturing Process Optimization
- Analyze production workflows using AI to improve efficiency and reduce waste
- Develop AI\-driven solutions for process automation and optimization
- Support Six Sigma and Lean initiatives with advanced analytics
- Work with scheduling team to build dynamic models that optimize throughput and cost based on machine availability, labor constraints, and order priority
- Deployment \& Integration
- Deploy models into production environments (edge devices, cloud, or on\-prem systems)
- Integrate AI solutions with existing manufacturing and quality systems
- Monitor model performance and ensure compliance with engineering and quality standards
- Cross\-Functional Collaboration
- Develop functional alignments by working with engineering, quality, operations, and IT teams
- Translate manufacturing and quality requirements into AI solutions
- Support audits and customer reviews requiring technical data and validation documentation
Education and Training:
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field
Minimum Qualifications:
- 3\+ years of experience in AI/ML development (manufacturing experience preferred)
- Strong programming skills in Python (TensorFlow, PyTorch, Scikit\-learn)
- Experience with statistical analysis and quality engineering methods
- Familiarity with PPAP, APQP, and core quality tools
- Experience with Industrial IoT (IIoT) and sensor data
- Familiarity with MES, ERP, and SCADA systems
- Knowledge of computer vision and deep learning
- Understanding of Six Sigma, SPC, MSA, FMEA, and control plans
- Experience developing engineering models tied to product or process validation
Physical Demands:
- Reporting to work for assigned shift, maintaining a physical presence and performing assigned work for the duration of assigned shift is required;
- Ability to work in a variety of environments which may include exposure to extreme heat, humidity, fumes or airborne particles, toxic or caustic chemicals, loud noise, and all weather conditions.
- The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- While performing the duties of this job, the employee is regularly required to talk or hear. The employee is frequently required to use hands to finger, handle, or feel and reach with hands and arms. The employee is occasionally required to stand, walk, sit, climb or balance and stoop, knee, crouch, or crawl. The employee must occasionally lift and/or move up to 75 pounds. Specific vision abilities required by this job include close vision.
Environmental Conditions:
- Ability to work in a variety of environments which may include exposure to extreme heat, humidity, fumes or airborne particles, toxic or caustic chemicals, loud noise, and all weather conditions.
Behavioral Traits:
- Core Competencies
+ Conflict Management – Recognize that conflict can be a valuable part of the decision\-making process. Comfortable with healthy conflict, and support and manage differences of opinion. Use consensus and collaboration to debate and resolve issues.
+ Team Management – Create and maintain functional work units. Understand human dynamics of team formation and maintenance. Develop and communicate clear team goals and roles. Provide guidance and management when appropriate. Foster a team atmosphere.
+ Integrity – Think and act ethically and honestly. Apply ethical standards of behavior to daily work activities. Take responsibility for their actions and foster a work environment where integrity is rewarded.
+ Positive Impact – Make positive impressions on those around them. Optimistic and enthusiastic about what they do. Energizes those around them.
+ Sensitivity – Value and respect the concerns and feelings of others. Communicate empathy toward others, respect for the individual, and appreciation of diversity among team members.
+ Talent Development – Keep a continual eye on the talent pool, monitoring skills and needs of all team members. Expand the skills of staff through training, coaching, and development activities related to current and future jobs. Evaluate and articulate present performance and future potential to create opportunities for better use of staff abilities. Identify developmental needs and assist individuals in developing plans to improve themselves.
- Leadership Competencies
+ Relationship Building/Sensitivity – Able to establish and maintain productive relationships. Good at interacting with people and devote appropriate time and energy to establish and maintain networks. Utilize relationships to facilitate business transactions.
+ Communications – Clear, frequent information. Offer full attention when others speak. Actively seek information from a variety of sources and disseminate it in a variety of ways. Cleary and articulately convey information to others in casual or informal situations. Able to interpret body language. Ability to organize and deliver public speeches that effectively inform or persuade others. Clearly and concisely composing informative and convincing memos, emails, letters, reports.
+ Drive/Energy – Display ah high level of energy and the motivation to sustain it over time. Ambitious and passionate about their role in the organization. Ability to maintain a healthy work/life balance.
+ Influence – Skilled at direction, persuading and motivating others. Ability to flex their style to direct, collaborate, or empower as the situation requires.
+ Organizing \& Planning – Display strong organizing and planning skills that allow them to be highly effective and efficient. Manage their time wisely, and effectively prioritize multiple competing tasks.
+ Problem solving \& Decision Making – Ability to identify problems, solve them, act decisively, and to show good judgement. Isolate causes from symptoms. Find balance between studying the problem and solving it.
Pay \& Benefits
Rate: In accordance with applicable pay transparency laws, the salary range for this position is (). Final compensation will be determined based on factors such as experience, education, qualifications, and other job\-related criteria. This pay range is provided to ensure transparency and compliance with local, state, and federal regulations.
- Job Type: Full time
- For a full list of benefits please visit: *anchorharvey.com/benefits*
- This is not a comprehensive list of duties. Duties may change without notice in management's sole discretion. Anchor Harvey is an at\-will employer, each employee is free to resign at any time, just as Anchor Harvey is free to terminate employment at any time without cause or notice.
Anchor Harvey is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Join Our World\-Class Workforce \| Anchor Harvey
Req Benefits: We offer a full complement of benefits that include medical, dental, and vision insurance, life insurance, travel insurance, disability insurance, a flexible spending account as well as a 401K plan.
Compensation: 150,000\-175,000
Salary Context
This $150K-$175K range is below the median 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 Anchor Harvey, 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. This role's midpoint ($162K) sits 26% below the category median. Disclosed range: $150K to $175K.
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.
Anchor Harvey AI Hiring
Anchor Harvey has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Schaumburg, IL, US. Compensation range: $175K - $175K.
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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