Interested in this AI/ML Engineer role at Howmet Aerospace?
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About This Role
Howmet Aerospace Inc. (“Howmet”) is currently in search of an Artificial Intelligence Engineer to join the Global Information Systems (GIS) organization at the Howmet Corporate Center in Pittsburgh, PA.
In this role you will be responsible for developing and deploying artificial intelligence models and surrounding applications and data pipelines. -
Major activities/Key challenges:
- Model Development and Training
+ Design and build machine learning and deep learning models tailored to solve specific business problems. + Train, evaluate, and fine-tune models using appropriate datasets and techniques.
- Application Integration
+ Deploy AI models into production and integrate them with existing systems or applications. + Design APIs or interfaces for models to interact with external applications.
- Data Management and pipeline development
+ Collect, preprocess, and clean datasets to ensure data quality and relevance. + Create and optimize pipelines for efficient data handling.
- Performance Optimization
+ Monitor the performance of deployed models and update them as needed. + Optimize model performance for speed, accuracy, and scalability.
- Research and Innovation
+ Stay updated with the latest AI and machine learning research and integrate new methodologies where applicable. + Experiment with state-of-the-art tools and technologies to improve current solutions.
- Collaboration and Communication
+ Work closely with cross-functional teams, including data engineers, application developers and analysts, and business stakeholders. + Translate complex AI concepts into actionable business insights.
Essential knowledge, skills and abilities:
- Strong verbal and written communications skills and ability to interact with customers, vendors, other IT professionals, and management; can communicate technical information effectively across all levels of the organization
- Practices continuous learning, interested in learning and mastering new technologies and techniques to improve processes and systems
- Sound decision making, capable of resolving open items swiftly and efficiently
- Strong aptitude to plan and prioritize own work to manage multiple priorities and meet deadlines
- Solid project management and organizational skills including documenting and optimizing processes
Basic Qualifications:
- Technical bachelor's degree from an accredited institution
- Minimum of 5 years of experience in Information Technology
- 2+ years prior experience in analytics and/or data science
- Working knowledge of SQL and Python
- Strong grasp of linear algebra, statistics, optimization techniques, and algorithm design.
- Employees must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is not available for this position
- Exposure to data engineering/data pipeline development
Preferred Qualifications:
- Bachelor’s degree in data science or artificial intelligence
- Experience using the following python packages + Machine Learning - TensorFlow, PyTorch, Scikit-learn, or similar frameworks. + Data Manipulation and visualization - Pandas, NumPy, and data visualization libraries such as Matplotlib or Seaborn.
- Familiarity with cloud platforms (Oracle, AWS, Google Cloud, Azure) and deployment tools like Docker and Kubernetes. Specifically, Oracle Cloud Infrastructure a plus.
- Familiarity with integrating cloud technologies with legacy applications
- Creating architecture with network segmentation in mind.
- Experience with NLP libraries such as SpaCy, NLTK, or Hugging Face Transformers is advantageous.
- Exposure to Python data packages and streaming technology, such as PySpark, Kafka, Apache Airflow and delta loading.
Salary Range: $90k - $115k/year approximation (actual compensation is subject to variation due to factors such as education, experience, skillset, and/org location).
Compensation and Benefits:
If a pay range is identified for this job, it is based on a good faith estimate at the time of this posting. Actual compensation is subject to variation due to factors such as education, experience, skillset, market/business considerations, and/or location.
Howmet Aerospace offers a competitive benefits package to support the health, financial well-being, and professional growth of our employees. Benefits include:
- Comprehensive medical, prescription, dental, and vision benefits coverage, including FSA and HSA account options
- Pre-tax and Roth 401(k) retirement savings plans offering employer matching funds and a separate employer retirement contribution
- Paid holidays and vacation time (eligibility, accrual, and usage are discretionary and governed by applicable policies)
- Paid parental leave and adoption assistance programs
- Life insurance and disability coverage plans
- Optional coverage plans for auto, home, legal, identity theft, and pet insurance
- Tuition Assistance Program and ongoing professional development opportunities
- Employee Assistance Program (EAP)
Our Story:
Howmet Aerospace Inc. (NYSE: HWM), headquartered in Pittsburgh, Pennsylvania, is a leading global provider of advanced engineered solutions for the aerospace and transportation industries. Our primary businesses focus on jet engine components, aerospace fastening systems, titanium structural parts and forged wheels. With $8.3 Billion in revenue in 2025, our products play a crucial role in enabling fuel efficiency and lightweighting, contributing to our customers’ success and making a positive impact on the world. To learn more about the way Howmet Aerospace Inc. is advancing the sustainability of our customers, markets, and communities where we operate, review the 2025 Environmental Social and Governance report at www.howmet.com/esg-report. Follow: LinkedIn, Twitter, Instagram, Facebook, and YouTube.
Equal Opportunity Employer:
Howmet is proud to be an Equal Employment Opportunity employer. We are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or other applicable legally protected characteristics.
If you need assistance to complete your application due to a disability, please email: [email protected]
Salary Context
This $90K-$115K range is in the lower quartile 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 Howmet Aerospace, 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 ($102K) sits 52% below the category median. Disclosed range: $90K to $115K.
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.
Howmet Aerospace AI Hiring
Howmet Aerospace has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pittsburgh, PA, US. Compensation range: $115K - $115K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 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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