1

Machine Learning Engineer Jobs in Pittsburgh, PA

... engineers Qualifications * U.S. Citizenship is required * Advanced degree, or bachelor's with at least 3 years of experience, in Data Science, Machine Learning or a related field Required Skills:

Machine Learning Systems Engineer

Pittsburgh, PA · On-site +1

$144K - $192K/yr

We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier ...

About the Team You'll lead the Machine Learning and FPT teams, working closely with the Director of Cloud Engineering and Director of Autonomy. Cross-departmentally, you'll collaborate with Product ...

About the Team You'll lead the Machine Learning and FPT teams, working closely with the Director of Cloud Engineering and Director of Autonomy. Cross-departmentally, you'll collaborate with Product ...

About the Team You'll lead the Machine Learning and FPT teams, working closely with the Director of Cloud Engineering and Director of Autonomy. Cross-departmentally, you'll collaborate with Product ...

Showing results 21-40

Machine Learning Engineer information

See Pittsburgh, PA salary details

$30.6K

$125K

$187.8K

How much do machine learning engineer jobs pay per year?

As of Aug 20, 2026, the average yearly pay for machine learning engineer in Pittsburgh, PA is $125,008.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $150,500.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Pittsburgh, PA?

The most popular types of Machine Learning Engineer jobs in Pittsburgh, PA are:

What are popular job titles related to Machine Learning Engineer jobs in Pittsburgh, PA?

For Machine Learning Engineer jobs in Pittsburgh, PA, the most frequently searched job titles are:

What cities near Pittsburgh, PA are hiring for Machine Learning Engineer jobs?

Cities near Pittsburgh, PA with the most Machine Learning Engineer job openings:

Infographic showing various Machine Learning Engineer job openings in Pittsburgh, PA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Remote job distribution, with an average salary of $125,008 per year, or $60.1 per hour.

Senior Machine Learning Engineer

Air

Pittsburgh, PA

$118K - $156K/yr

Full-time

Re-posted 29 days ago


Job description

Company Description
Air is the leader in Enterprise Readiness. Our mission is to establish readiness as a real-time condition that is continuously achieved. Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered. Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers. By revealing true capacity, exposing real constraints, coordinating resources, and executing at the speed of operational demands, the front line gets what it needs to succeed.
Job Description
We are seeking a Natural Language Processing expert to join our team and help us build cutting-edge machine learning technology that will replace complex, time-consuming, manual processes with automation and intelligence that helps Air end-users make scientific and analytical decisions. We're looking for world-class talent to join our team where you will have the opportunity to help develop a wide range of solutions that transform natural language data into useful features for classification algorithms, as well as implement the latest technology to improve user search functionality.
In order to do this job well, you must be a curious and eager problem solver with a hunger for building well-designed models and solving problems in an ambiguous space. You share our intolerance of mediocrity. You're uber-smart, challenged by figuring things out and producing simple solutions to complex problems. Knowing there are always multiple answers to a problem, you know how to engage in a constructive dialogue to find the best path forward. You're scrappy. We like scrappy.
This role is a full-time position located out of our office in Pittsburgh, PA.
This role may require up to 10% travel
Scope of Responsibilities
  • Inform and implement the design and development of NLP applications to enhance the intelligence and efficiency of our data analytics software-as-a-service platform
  • Review, verify, and aggregate the most appropriate annotated datasets for the best-supervised learning methods
  • Ability to work with taxonomy experts to create and validate dataset annotation
  • Use well-formed and effective text representation to change and adapt natural language documents into user-friendly features
  • implement the latest technology to improve user search functionality as well as integrate state of the art LLM models into our Air Enterprise Readiness platform to enhance user experience.
  • Develop new algorithms and modeling techniques and conduct sound experiments to verify model results and integrate models into the live production system
  • Establish meaningful criteria for evaluating algorithm performance and suitability
  • Implement working, scalable, production-ready models and code
  • Keep up to date with Machine Learning best practices and evolving open-source frameworks
  • Regularly seek out innovation and continuous improvement, finding efficiency in all assigned tasks
  • Collaborate closely with fellow taxonomists, software engineers, data scientists, data engineers, and QA engineers
Qualifications
  • U.S. Citizenship is required
  • Advanced degree, or bachelor's with at least 3 years of experience, in Data Science, Machine Learning or a related field
Required Skills:
  • Minimum 3 years experience with hands-on development of NLP models
  • In-depth understanding of NLP methods for text representation, semantic extraction techniques, data structures, and modeling
  • Practical experience in building, developing, and productionizing both supervised and unsupervised machine learning models
  • Advanced software skills in Python
  • Advanced ability in forming SQL queries
  • A strong desire to learn, investigate, and implement cutting-edge technologies
  • Ability to work collaboratively throughout the design process.

Desired Skills:

  • Current possession of a U.S. security clearance, or the ability to obtain one with our sponsorship
  • Experience in or exposure to the nuances of a startup or other entrepreneurial environment
  • Experience in deploying ML models in Kubernetes environments
  • Experience developing custom training sets for large language models
  • Experience productionizing transformer-based models
We firmly believe that past performance is the best indicator of future performance. If you thrive while building solutions to complex problems, are a self-starter, and are passionate about making an impact in global security, we're eager to hear from you.
Air is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans status or any other characteristic protected by law.