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Machine Learning Engineer Opt Jobs in Philadelphia, PA

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Machine Learning Engineer

Philadelphia, PA

As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain and improve model inference services. You will learn and apply new techniques from open source ...

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Machine Learning Engineer Opt information

See Philadelphia, PA salary details

$31.8K

$129.9K

$195.3K

How much do machine learning engineer opt jobs pay per year?

As of Jun 9, 2026, the average yearly pay for machine learning engineer opt 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 are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

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

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

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 a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What are some common challenges Machine Learning Engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.
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Machine Learning Engineer Opt jobs near you

Machine Learning Engineer

Guru Schools

Philadelphia, PA • Hybrid

Other

Posted 7 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

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