Software Machine Learning Engineer

Benefits

Parental leave

4 weeks Source: https://teradyne.cn/wp-content/uploads/2020/03/2020-Open-Enrollment-Newsletter.pdf. source Last checked May 7, 2026.

Non-birth-parent leave

4 weeks Source: https://teradyne.cn/wp-content/uploads/2020/03/2020-Open-Enrollment-Newsletter.pdf. source Last checked May 7, 2026.

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

Not verified

Childcare support

Not verified

Learning budget

Not verified

Verification

Source-linked last checked May 7, 2026

Salary

$116K-$186K not verified - source not recorded; timestamp not recorded

401(k) match

Not verified

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

35% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

Not verified

Weekend work

Not verified

Application

Cover letter

Not verified

Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Software Machine Learning Engineer North Reading, MA Our Purpose TERADYNE, where experience meets innovation and driving excellence in every connection. We are fueled by creativity and diversity of thought and in our workforce. Our employees are supported to innovate and learn something new every day. We cultivate a culture of inclusion for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team - one that makes better decisions, drives innovation and delivers better business results. Opportunity Overview As a Machine Learning Engineer, you will design, develop, and deploy applied AI solutions with a good knowledge on graph machine learning, reinforcement learning, and interpretable AI. You will work closely with cross-functional teams to build scalable ML systems that model complex relationships in engineering data, optimize decision-making processes, and provide transparent, explainable insights. This role emphasizes hands-on development, experimentation, and collaboration rather than technical leadership. Design and implement pipelines for training, evaluation, and deployment of ML models. Apply graph ML methods to model relationships in structured and unstructured data. Build and experiment with reinforcement learning algorithms (e.g., policy gradients, PPO, Q-learning) for optimization and decision-making tasks. Incorporate interpretability and explainability techniques (e.g., SHAP, LIME, attention-based methods) into ML systems. Collabor

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