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Machine Learning Engineer Opt Jobs in Auburn, MA

As a Principal Data Engineer , you will be a senior technical contributor who partners closely with ... Support analytical and machine learning workflows from raw data ingestion through downstream ...

As a Principal Data Engineer , you will be a senior technical contributor who partners closely with ... Support analytical and machine learning workflows from raw data ingestion through downstream ...

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Machine Learning Engineer Opt information

See Auburn, MA salary details

$31.5K

$128.7K

$193.4K

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

As of May 29, 2026, the average yearly pay for machine learning engineer opt in Auburn, MA is $128,682.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,400.00 and $154,900.00 per year, depending on experience, location, and employer.

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.

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 job categories do people searching Machine Learning Engineer Opt jobs in Auburn, MA look for? The top searched job categories for Machine Learning Engineer Opt jobs in Auburn, MA are:
What cities near Auburn, MA are hiring for Machine Learning Engineer Opt jobs? Cities near Auburn, MA with the most Machine Learning Engineer Opt job openings:

Senior Machine Learning Engineer

Definitive Healthcare, US

Framingham, MA

$112K - $210K/yr

Other

Medical, Dental, Vision, Retirement, PTO

Posted 8 days ago


Job description

We are looking for a Senior Machine Learning Engineer to lead the design and implementation of cutting-edge AI/ML systems that deliver transformative business outcomes. In this role, you will take ownership of end-to-end ML solutions, from architecture and modeling to production and performance optimization.

From architecting end-to-end ML solutions to shaping technical strategy, your work will have a broad and lasting impact on customer experience and operational efficiency. The ideal candidate brings extensive experience in applied machine learning, deep software engineering expertise, and a track record of mentoring teams and delivering production-ready models at scale. This is a high-impact, full-stack ML role that blends research, engineering, and leadership, with the opportunity to shape both the company's technical foundation and product direction. 

What You'll Do

ML Systems Development & Deployment

  • Lead the design and implementation of scalable, production-grade ML systems in cloud environments with a focus on performance, reliability, and reproducibility. 
  • Collaborate with product managers and senior stakeholders to define and prioritize ML initiatives aligned with business goals. 

Data Pipeline & Feature Engineering

  • Oversee the architecture and evolution of data pipelines for multi-terabyte datasets, ensuring efficiency and reliability.
  • Guide the development of high-impact features and label sets across diverse domains such as healthcare and consumer analytics

Experimentation & Model Management

  • Lead experimentation strategy, including design of A/B tests, advanced validation methods, and lifecycle management using tools like MLflow and Databricks.
  • Drive continual model improvement through advanced techniques such as automated retraining, model decay analysis, and bias mitigation. Innovation & Prototyping
  • Champion rapid prototyping and proof-of-concept development to evaluate emerging technologies and ML techniques.
  • Lead technical explorations into new ML architectures (e.g., foundation models, causal inference, time series deep learning).

Cross-Functional Collaboration

  • Serve as a technical leader and trusted advisor, working closely with product, engineering, data, and executive teams to shape end-to-end ML solutions. Code Quality & Documentation
  • Set standards for code quality, performance, and documentation, and mentor junior engineers in best practices

What You'll Bring

  • Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field (or equivalent practical experience).
  • 5+ years of industry experience as an ML Engineer, Data Scientist, or Data Engineer, with a focus on deploying and scaling ML systems.
  • Deep expertise in Python, SQL, and PySpark for distributed data processing, with proficiency in libraries like scikit-learn, PyTorch, and XGBoost.
  • Proven experience designing robust ML pipelines, leveraging tools like MLflow or equivalent.
  • Strong command of ML frameworks (e.g., scikit-learn, TensorFlow, XGBoost, PyTorch).
  • Hands-on experience deploying models in cloud-based environments (AWS, GCP, Azure, and Databricks).
  • Proven ability to manage end-to-end ML lifecycles at scale, including data ingestion, training, evaluation, deployment, and monitoring.
  • Excellent communication skills and demonstrated ability to influence cross-functional teams.

Preferred Qualifications

  • Experience working with healthcare claims, EHR, or life sciences datasets.
  • Advanced degree (M.S. or Ph.D.) in Computer Science, Data Science, or related technical field.
  • Strong knowledge of MLOps practices including CI/CD for ML, automated retraining, and model versioning.
  • Experience with deep learning architectures for time series forecasting, sequential data, or hierarchical modeling.
  • Proficient in designing evaluation protocols and defining performance metrics to rigorously assess model effectiveness and drive data-driven decision-making.
  • Comfortable operating in fast-paced, high-ownership environments, and able to prioritize multiple high-impact projects

Compensation and Benefits

The salary range for this position is $112,000 - $210,000 per year, which represents the base pay the company reasonably and in good faith expects to pay for this role. Actual compensation will depend on relevant experience, skills, and qualifications. 

This role is also eligible to participate in the company's annual bonus program, subject to individual and company performance. 

All employees receive standard benefits, including medical, dental, and vision coverage, unlimited paid time off, and participation in the company's 401(k) plan with employer contribution.Â