ZipRecruiter

Log In

1

Machine Learning Engineer Jobs in Philadelphia, PA

Comcast

Machine Learning Engineer 3-7881

Philadelphia, PA · On-site +1

$115.50K - $138.70K/yr

... both software engineering and machine learning sides of projects by implementing, rening, and validating machine learning algorithms for products and applications; take action on existing ...

next page

Showing results 1-20

People also search for

All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Philadelphia, PA

Machine Learning Engineer information

See Philadelphia, PA salary details

$31.8K

$129.9K

$195.3K

How much do machine learning engineer jobs pay per year?

As of May 29, 2026, the average yearly pay for machine learning engineer in Philadelphia, PA is $129,939.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,400.00 and $156,400.00 per year, depending on experience, location, and employer.

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 are Machine Learning Engineers?

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 jobs make $3,000 a month without a degree?

A Machine Learning Engineer typically requires a degree, but roles such as data annotator, technical support specialist, or freelance programmer can sometimes earn around $3,000 monthly without a formal degree, especially with relevant skills and experience. These jobs often involve self-taught skills, online certifications, or on-the-job training and may require proficiency in tools like Python or cloud platforms.

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.

More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Philadelphia, PA? The most popular types of Machine Learning Engineer jobs in Philadelphia, PA are:
What are popular job titles related to Machine Learning Engineer jobs in Philadelphia, PA? For Machine Learning Engineer jobs in Philadelphia, PA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer jobs in Philadelphia, PA look for? The top searched job categories for Machine Learning Engineer jobs in Philadelphia, PA are:
What cities near Philadelphia, PA are hiring for Machine Learning Engineer jobs? Cities near Philadelphia, PA with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you

Machine Learning Engineer

Guru Schools

Philadelphia, PA • On-site

Full-time

Posted 28 days ago

Job description

Overview:
Machine Learning Engineer
Philadelphia, PA OR Washington, DC | Hybrid: 3-4 days/week
9 + Months
Role:
Design and validate ML models that support engineering tooling teams.
Enhance existing AIML automation tools (e.g., Speech data), implement LLM prompt interactions, and use LLMs to test LLMs - with a strong focus on product quality.
Key Responsibilities:
Build & enhance ML/AI models for validation and automation
Implement prompt-based LLM interactions
Collaborate across tooling squads and cross-functional teams
Contribute to POC development in AI/ML & Computer Vision
Requirements:
4+ years overall experience
1+ year hands-on ML model experience
Strong quality-focused mindset with LLM expertise
NLP, data engineering & model deployment experience
Tech Used:
GPT, LLMs, NLP, internet-developed tools
Interview Process:
2 Rounds
Skills:
Design and validate ML models that support engineering tooling teams. Enhance existing AIML automation tools (e.g., Speech data), implement LLM prompt interactions, and use LLMs to test LLMs

Apply