Job description

We are the global test and automation specialists, powering next-generation technologies through sophisticated solutions. Behind every electronic device you use, Teradyne's test technology ensures your device works right the first time, every time! Our portfolio of automation solutions help manufacturers to develop and deliver products quickly, efficiently and cost-effectively. Together, Teradyne companies deliver manufacturing automation across industries and applications around the world! We attract, develop, and retain a high-performance workforce, comprised of people with diverse backgrounds and a shared drive for excellence. We strive to foster a positive and inclusive work environment that helps employees, and communities, thrive. 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 Senior ML / AI Engineer at Teradyne, you will design, build, and operationalize the machine learning and AI solutions that power our IT organization, and you will prepare the team—technically and operationally—to own the AI solutions Teradyne develops internally. Reporting to the Enterprise AI/Data Product Manager within the Enterprise Architecture and Data organization, you will bridge solution development and production ownership, ensuring our AI solutions are reliable, governed, and sustainable long after they are first built. This is a deeply technical, hands-on role spanning classical machine learning and modern generative and agentic AI. You will develop ML/AI solutions requested by the IT organization—predictive and classification models, GenAI assistants, RAG workflows, and intelligent automation—and engineer the MLOps/LLMOps practices, pipelines, and playbooks that allow the team to operate and continuously improve them. You will work across our enterprise AI stack—primarily Microsoft Azure (Azure AI Foundry and Microsoft Copilot Studio), Anthropic Claude, and Google Vertex AI and Snowflake Cortex AI—grounding solutions in trusted enterprise data and integrating with enterprise systems through MCP servers and secure APIs. Your work ensures Teradyne’s AI investments move from experimentation to durable, production-grade capabilities. ML/AI Solution Development for IT • Partner with the AI Enablement Team and the IT organization to translate business problems into ML/AI solutions—predictive and classification models, GenAI assistants, RAG workflows, and intelligent process automation. • Design, develop, train, evaluate, and deploy end-to-end ML and generative AI solutions using Azure AI Foundry, Microsoft Copilot Studio, Google Vertex AI, and Snowflake Cortex AI. • Build and integrate AI agents with enterprise data sources, APIs, and MCP servers, grounding models in proprietary Teradyne data through embedding and retrieval pipelines. • Apply rigorous experimentation, model selection, and evaluation to deliver solutions that are accurate, performant, and fit for purpose. Business Outcome: Deliver high-value, production-ready ML/AI solutions that solve real IT and business problems and demonstrate measurable impact. Operationalizing & Owning Internally Developed AI Solutions • Own the transition of internally developed AI solutions from build to production, defining and certifying the release-readiness in accordance to standard set by the AI Platform Operations Team. • Implement full model lifecycle management—versioning, model registry, retraining, promotion, and deprecation—so solutions remain accurate and maintainable over time. • Define drift, degradation, and retraining criteria for deployed models and agents, and drive tuning and remediation of the underlying solution for the AI Platform Operations Team to execute scheduled retraining and registry operations against these criteria. • Partner with the AI Platform Operations Engineer, providing the technical input—model and agent behavior, dependencies, failure modes—needed for the run-books, on-call processes, and handoff standards they own and author. Business Outcome: Ensure internally developed AI solutions are durable, well-owned, and continuously improved rather than one-off builds. MLOps / LLMOps Engineering & Automation • Engineer reusable, multi-environment MLOps/LLMOps pipelines using Azure DevOps or GitHub Actions to automate the full lifecycle from training and evaluation to deployment. • Build CI/CD, testing, and infrastructure-/configuration-as-code for models, prompts, and agents to make releases repeatable, testable, and auditable. • Leverage tooling such as Azure Machine Learning, MLflow, and model/feature registries to standardize experimentation, tracking, and deployment. • Establish automated evaluation and regression testing (including offline and online evaluation) for both predictive and generative solutions. • Design MLOps/LLMOps pipelines for the AI Platform Operations Team to operate in production once deployed, ensuring pipelines are supportable, observable, and handoff-ready. Business Outcome: Provide standardized, automated delivery pipelines that increase speed, quality, and reliability of AI solution releases. Team Technical & Process Readiness • Establish the technical standards, reference patterns, and reusable frameworks the team needs to build and operate AI solutions consistently. • Define and document the processes and playbooks—development, evaluation, deployment, monitoring, and support—that prepare the team to own AI solutions end-to-end. • Mentor and upskill engineers on ML/AI, MLOps/LLMOps, and agentic AI practices, raising the overall capability and readiness of the team. • Champion adoption of best practices and lessons learned across projects to mature the team’s AI operating model. Business Outcome: Ensure the team is technically and process-ready to own and scale AI solutions with confidence and consistency. Responsible AI, Governance & Reliability • Implement responsible AI practices and technical guardrails for data handling, model usage, and safe, compliant AI behavior. • Apply AI security controls to models, agents, and RAG workflows, mitigating risks such as prompt injection and unauthorized data access. • Build observability, logging, and audit trails for AI solutions to support explainability, compliance, and troubleshooting. • Monitor solution performance, quality, reliability, and cost, and drive continuous improvement against defined metrics. Business Outcome: Reduce AI risk and improve trust, compliance, and reliability across Teradyne’s deployed AI solutions. All About You We seek individuals who share our passion and determination. Our commitment to customer success drives us to go the extra mile. If you’re ready to join us in this mission, take a closer look at the minimum criteria for the position. • 8–10 years of experience in ML/AI or software engineering, including at least 3 years building and operating production ML/AI systems and recent hands-on experience with generative and agentic AI. • Bachelor’s or advanced degree in Computer Science, Data Science, Engineering, or a related field. • Strong proficiency in Python and SQL, and experience with ML frameworks such as scikit-learn, PyTorch, or TensorFlow. • Hands-on experience building and deploying ML and generative AI solutions with one or more enterprise AI platforms: Azure AI Foundry, Microsoft Copilot Studio, Anthorpic Claude, Cursor, Google Vertex AI, and Snowflake Cortex AI. • Proven MLOps/LLMOps experience—multi-environment pipelines, CI/CD (Azure DevOps or GitHub Actions), Azure Machine Learning, MLflow, and model lifecycle management. • Experience with agentic AI orchestration frameworks, RAG, vector databases and embeddings, prompt engineering, and integration via MCP servers and APIs. • Experience transitioning AI solutions from development into production ownership, including monitoring, drift detection, retraining, and support. • Strong knowledge of model evaluation and testing, AI observability, responsible AI, AI security, and governance frameworks. • Multicloud experience with Microsoft Azure as primary and Google Cloud as secondary, integrating with enterprise data on Snowflake and Microsoft Fabric. • Poven ability to mentor and upskill teams, fostering a culture of innovation and learning. • Strong collaboration and communication skills, with the ability to work across technical and business teams. • Analytical mindset with a focus on delivering measurable business outcomes. Compensation: The base salary range for this role is $133,900-$223,900. This range is a good faith estimate, and the amount of base salary will correspond with experience and skill set. This range can also fluctuate depending on demand and location. Incentive Plan: This job is eligible for discretionary bonus(es) based on financial performance. Benefits: Teradyne offers a variety of robust health and well-being benefit programs, including medical, dental, vision, Flexible Spending Accounts, retirement savings plans, life and disability insurance, paid vacation & holidays, tuition assistance programs, and more. Please click here to see details. #LI-REMOTE

Requirements

Must-haves

Opportunity Overview
ML/AI Solution Development for IT
Partner with the AI Enablement Team and the IT organization to translate business problems into ML/AI solutions—predictive and classification models, GenAI assistants, RAG workflows, and intelligent process automation.
Design, develop, train, evaluate, and deploy end-to-end ML and generative AI solutions using Azure AI Foundry, Microsoft Copilot Studio, Google Vertex AI, and Snowflake Cortex AI.
Build and integrate AI agents with enterprise data sources, APIs, and MCP servers, grounding models in proprietary Teradyne data through embedding and retrieval pipelines.
Apply rigorous experimentation, model selection, and evaluation to deliver solutions that are accurate, performant, and fit for purpose.
Business Outcome: Deliver high-value, production-ready ML/AI solutions that solve real IT and business problems and demonstrate measurable impact.
Operationalizing & Owning Internally Developed AI Solutions
Own the transition of internally developed AI solutions from build to production, defining and certifying the release-readiness in accordance to standard set by the AI Platform Operations Team.
Implement full model lifecycle management—versioning, model registry, retraining, promotion, and deprecation—so solutions remain accurate and maintainable over time.
Business Outcome: Ensure internally developed AI solutions are durable, well-owned, and continuously improved rather than one-off builds.
MLOps / LLMOps Engineering & Automation
Engineer reusable, multi-environment MLOps/LLMOps pipelines using Azure DevOps or GitHub Actions to automate the full lifecycle from training and evaluation to deployment.
Build CI/CD, testing, and infrastructure-/configuration-as-code for models, prompts, and agents to make releases repeatable, testable, and auditable.
Leverage tooling such as Azure Machine Learning, MLflow, and model/feature registries to standardize experimentation, tracking, and deployment.
Establish automated evaluation and regression testing (including offline and online evaluation) for both predictive and generative solutions.
Design MLOps/LLMOps pipelines for the AI Platform Operations Team to operate in production once deployed, ensuring pipelines are supportable, observable, and handoff-ready.
Business Outcome: Provide standardized, automated delivery pipelines that increase speed, quality, and reliability of AI solution releases.
Team Technical & Process Readiness
Establish the technical standards, reference patterns, and reusable frameworks the team needs to build and operate AI solutions consistently.
Define and document the processes and playbooks—development, evaluation, deployment, monitoring, and support—that prepare the team to own AI solutions end-to-end.
Mentor and upskill engineers on ML/AI, MLOps/LLMOps, and agentic AI practices, raising the overall capability and readiness of the team.
Champion adoption of best practices and lessons learned across projects to mature the team’s AI operating model.

Nice-to-haves

Incentive Plan: This job is eligible for discretionary bonus(es) based on financial performance.

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mlaimachine learningazureci/cdgopythonsql

Platform: successfactors
Effort: medium

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Dental

Vision