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About This Role
Artificial Intelligence (AI) Engineer
Country United States of America State / County Pennsylvania City Jenkintown, Langhorne Division Fasteners Company Jenkintown Function Information Technology Workplace Type On-Site Position type Full Time
Opportunity ID 24747
Job Description Position Summary:
The AI Engineer is a hands‑on, shop‑floor engineer who applies AI and data‑enabled tools to improve safety, quality, throughput, productivity, and time‑to‑proficiency in skilled labor roles. This role partners directly with Engineering, Quality, IT, Maintenance, EHS, Supply Chain, and HR to translate real manufacturing problems into practical, scalable AI applications that are adopted and sustained on the floor.
The role is a Manufacturing Engineer / AI Engineer hybrid, spending approximately 50–60% of time developing and maintaining plant AI applications and 40–50% of time in manufacturing, working alongside operators, supervisors, and maintenance teams to ensure solutions reflect real process behavior and are embedded into standard work.
Key Responsibilities
Plant AI Enablement & Manufacturing Partnership
- Partner with Engineering, Quality, Maintenance, EHS, Supply Chain, and HR to identify and prioritize high‑value AI use cases tied to safety, quality, throughput, productivity, and workforce capability.
- Build and manage a plant AI opportunity pipeline, including use cases, value hypotheses, owners, required data, timing, and success metrics.
- Define clear problem statements, requirements, and KPIs (e.g., defect escape reduction, downtime reduction, cycle time improvement, injury risk reduction, faster time to proficiency).
- Lead pilots from concept through shop‑floor adoption, including data readiness, trial design, operator input, training, launch, and sustainment.
- Ensure AI solutions are simple, explainable, and usable for operators and supervisors, integrated into standard work and leader routines.
- Identify and mitigate operational and safety risks (failure modes, false positives/negatives, bias, safety impacts) and ensure controls and escalation paths are in place.
Manufacturing & Process Engineering
- Improve manufacturing processes across machining, forming, assembly, and inspection operations.
- Lead root cause analysis related to scrap, rework, downtime, delinquencies, training‑related errors, and safety risks.
- Develop, improve, and sustain standard work, process flows, layouts, tooling, and capability studies.
- Support equipment commissioning, process optimization, and reliability improvement in partnership with Maintenance and Operations.
AI Application Development (Plant–IT Collaboration)
- Own hands‑on development, deployment, and sustainment of lightweight AI‑enabled plant applications (prototypes through targeted production features) using Division and Corporate IT/AI standards for architecture, security, and governance.
- Serve as the manufacturing product owner for plant AI applications by defining requirements, validating outputs against shop‑floor reality, and ensuring usability for end users.
- Lead the end‑to‑end lifecycle for plant AI solutions (design, development, testing, release, training, sustainment) and escalate design decisions and risks as needed.
- Develop and maintain solutions such as Databricks Apps, internal dashboards, decision tools, and AI‑assisted workflows that operationalize manufacturing use cases.
- Integrate APIs, model endpoints, and data services into user‑facing tools; document assumptions, controls, and escalation paths.
- Use Git and follow agreed release, testing, and change‑management practices; provide Tier 1 support and coordinate enhancements with IT and Corporate AI teams.
Workforce Capability & Time‑to‑Proficiency Improvement
- Apply AI‑enabled training and development tools to reduce time to proficiency in key skilled labor roles (e.g., machinists, thread roll operators, maintenance technicians, inspectors).
- Partner with Engineering, Operations, and HR to identify skill gaps, high‑error steps, and learning friction points.
- Develop AI‑supported tools such as digital work instructions, visual job aids, troubleshooting assistants, and skill‑progression checkpoints.
- Enable supervisors and trainers with data‑driven insights to focus coaching, standardize training across shifts, and reinforce correct behaviors.
- Validate training effectiveness through faster readiness for independent work, reduced early‑stage defects and downtime, and improved retention.
Data, Continuous Improvement & Change Leadership
- Use MES, SPC, quality systems, downtime tracking, sensor/PLC data, and learning data to drive improvement.
- Validate AI outputs through hands‑on observation and process confirmation.
- Establish KPIs, control plans, and visual management to sustain gains.
- Act as a change agent by training and coaching operators and supervisors and embedding improvements into standard work and leader routines.
Experience & Education
Required Qualifications:
- Familiarity with Git and basic software delivery practices.
- Applied AI and digital fluency with light coding capability; able to build prototypes and small production applications.
- Proficiency with Python or similar scripting; familiarity with simple UIs (Streamlit/Dash‑style) and REST APIs.
Preferred Qualifications:
- Strong manufacturing engineering fundamentals (process capability, variation reduction, PFMEA/control plans, standard work).
- Proven structured problem solving (5 Why, fishbone, DOE where appropriate) and Lean/CI leadership.
- Experience with CNC, forming, or precision manufacturing equipment
- Working knowledge of MES, SPC, quality systems, downtime systems, and industrial data sources.
- Familiarity with aerospace, automotive, medical or other highly regulated manufacturing fields
Education:
- Bachelor’s degree in Manufacturing Engineering, Mechanical Engineering, Computer Science, or related field; experience in a high‑mix / high‑volume manufacturing environment strongly preferred.
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.
Relocation is authorized for this position.
National Security and Export Controls:
This position requires use of information or access to production processes subject to national security controls under U.S. export control laws and regulations (including, but not limited to the International Traffic in Arms Regulations (ITAR) and the Export Administration Regulations (EAR)). To comply with those regulations, this position may require applicants to be U.S. Persons (i.e., U.S. citizens, U.S. lawful permanent residents, protected individuals as defined by 8 U.S.C. 1324b(a)(3)), or eligible to obtain the required export authorizations from the U.S. Department of State or the U.S. Department of Commerce. About PCC With more than 120 operations and approximately 20,000 employees worldwide, Precision Castparts Corp. is the market leader in manufacturing large, complex structural investment castings, airfoil castings, forged components, aerostructures and highly engineered, critical fasteners for aerospace applications. In addition, we are the leading producer of airfoil castings for the industrial gas turbine market. We also manufacture extruded seamless pipe, fittings, and forgings for power generation and oil & gas applications; commercial and military airframe aerostructures; and metal alloys and other materials for the casting and forging industries. With such critical applications, we insist on quality and dependability – not just in the materials and products we make, but in the people we recruit.
PCC is relentless in its dedication to being a high-quality, low-cost and on-time producer; delivering the highest value to its customers while continually pursuing strategic, profitable growth.
In 2016, Berkshire Hathaway acquired Precision Castparts Corp.
Precision Castparts Corp. and its affiliates do not accept unsolicited resumes from search firms or employment agencies. Unsolicited resumes will become the property of Precision Castparts Corp & its affiliates, and no fee will be paid.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Precision CastParts, 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 $212,040 based on 13,780 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 $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Precision CastParts AI Hiring
Precision CastParts has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Jenkintown, PA, US, Clackamas, OR, US, US. Compensation range: $168K - $168K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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