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About This Role
Neural Network Architect
Country United States of America State / County Various Locations City Various Locations Division Metals Company TIMET Division Function Quality Workplace Type On-Site Position type Full Time
Opportunity ID 24552
Job Description About the Role
We are building a new AI function to modernize how our metals operations generate, connect, and act on data. The Neural Network Architect will lead the design and implementation of a unified data and AI infrastructure that links our Enterprise Resource Planning (ERP), shop floor process/control systems (SCADA, PLC, MES/historian data), Laboratory Information Management Systems (LIMS), cost accounting systems, and Customer Relationship Management (CRM) platforms into a single, coherent architecture.
This is a foundational, cross-functional role. The person in this seat will not just design neural network models, but they will architect the data backbone that makes reliable, production-grade AI possible across the enterprise: from predictive quality control and yield optimization on the shop floor, to demand forecasting that links CRM and production data, to cost-to-serve models that tie financials to real-time operations.
SCOPE:
While "Neural Network Architect" is the title, this role is as much about systems integration, data engineering, and information architecture** as it is about neural network design. Candidates should be equally comfortable designing a data pipeline between a PLC historian and an ERP as they are designing or evaluating a deep learning model. We are not looking for a pure research scientist, rather we are looking for someone who can turn fragmented industrial data into an infrastructure that AI systems (including neural networks) can reliably run on.
Key Responsibilities
Unified Infrastructure Design
- Architect a scalable, secure data platform that integrates ERP, shop floor/process control systems, LIMS, cost/financial systems, and CRM into a shared data layer.
- Define data models & schemes to link above data with data security and pathways for statistical models to be integrated
- Establish real-time and batch data pipelines connecting operational technology (OT) systems on the shop floor with information technology (IT) systems (ERP, CRM, finance).
Neural Network & AI Model Development
- Design, train, and deploy neural network and other ML models on top of the unified data infrastructure for use cases such as predictive maintenance, quality/defect prediction, yield optimization, demand forecasting, and cost anomaly detection.
- Evaluate where deep learning is the right tool versus simpler statistical or rules-based approaches, given industrial data constraints (sparsity, sensor noise, labeling challenges).
- Build MLOps practices for model versioning, monitoring, and retraining as plant and market conditions evolve.
Systems Integration & Governance
- Partner with IT, OT/automation engineers, lab managers, finance, and sales operations to map data sources, ownership, and access rules across departments.
- Establish data governance standards: data quality, lineage, security, and access control across sensitive systems (cost data, customer data, process parameters).
- Select and implement integration tooling (APIs, middleware, historians, data buses) appropriate for connecting legacy industrial systems with modern cloud/on-prem AI infrastructure.
Leadership & Strategy
- Serve as the technical authority on how AI infrastructure should be built as the company's AI department scales.
- Advise leadership on architecture trade-offs, build-vs-buy decisions, and sequencing of integration work (e.g., which systems to connect first for fastest ROI).
- Document architecture decisions and mentor future data engineers/ML engineers hired into the department.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Data Engineering, Electrical Engineering, Industrial Engineering, or related field (PhD a plus but not required).
- 7+ years of experience in data architecture, data engineering, or ML/AI engineering, including at least 3 years working with industrial or manufacturing data (OT/IT integration experience strongly preferred).
- Demonstrated experience designing and deploying neural networks or other ML models in production (not just research/prototype settings).
- Hands-on experience integrating disparate enterprise systems — direct experience with ERP platforms (SAP, Oracle, Infor, etc.), MES/historian systems (e.g., OSIsoft PI, Ignition, Wonderware), and CRM platforms (Salesforce, Dynamics, etc.) is highly valued.
- Strong data engineering skills: SQL, Python, data pipeline tools (Airflow, dbt, Kafka, or similar), and cloud or on-prem data platforms (Azure, AWS, GCP, Snowflake, Databricks).
- Working knowledge of ML/DL frameworks (PyTorch, TensorFlow) and MLOps tooling.
- Understanding of industrial control systems architecture (SCADA, PLC, Purdue Model / ISA-95 levels) and the security considerations of bridging OT and IT networks.
Preferred Qualifications
- Prior experience in metals, mining, or heavy manufacturing industries.
- Familiarity with LIMS platforms and quality/metallurgical data structures (chemistry, mechanical properties, heat treatment data).
- Experience with cost accounting or ERP-based financial data models (standard costing, activity-based costing).
- Experience building a data/AI function from the ground up in a traditionally non-digital organization.
- Knowledge of relevant industrial data standards (ISA-95, MTConnect, OPC UA).
Success Metrics for This Role (First 18 Months)
- A functioning, documented unified data architecture connecting at least ERP + shop floor + LIMS as a first milestone, with CRM and cost systems integrated in subsequent phases.
- At least one production AI/ML use case (e.g., a quality prediction or yield model) running on top of the new infrastructure.
- Clear data governance and access framework adopted across departments.
- A scalable foundation that allows the AI department to add additional models and use cases without re-architecting from scratch.
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. For informational purposes only, the range of pay for this role is $135,000 to $168,000 US Dollar (USD) Salaried based on a variety of factors. For informational purposes only, this role is eligible to participate in the Company’s Quarterly Cash Bonus Plan, paid out at the quarterly bonus factor of 2.5X the Company’s bonus multiple, based on the Company’s performance in the applicable quarter. Employees will receive 120 hours paid time off every year. Employees will also receive 10 paid holidays. Employees (and their families) are eligible to participate in the company's medical, dental, vision, and basic life insurance. Employees are eligible to enroll in the Company’s 401(k) plan. 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. Company Profile 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, led by Chairman and CEO Warren E. Buffett, 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.
Salary Context
This $135K-$168K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($151K) sits 29% below the category median. Disclosed range: $135K to $168K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Precision CastParts AI Hiring
Precision CastParts has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $168K - $168K.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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