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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. As Senior Machine Learning Engineer, you are Teradyne's highest individual contributor technical authority in ML. You own the end-to-end technical excellence of Teradyne's machine learning systems: from research and experimentation to production deployment and ongoing model performance. You will define modeling standards, set the engineering bar for the entire ML team, and take personal ownership of the most complex and highest-impact ML problems. Your work will span time-series analysis of semiconductor parametric test data, computer vision for defect detection, adaptive test optimization, and applied LLM systems. You will be the go-to technical voice when the team faces hard problems and the mentor of junior engineers around you.
Job Responsibility
Own the technical direction and quality of all ML model development across Teradyne's AI initiatives, setting and enforcing engineering standards across the team
Lead end-to-end development of production ML systems: data ingestion and feature engineering, model architecture design, training pipelines, evaluation frameworks, and deployment
Design and implement novel ML approaches tailored to Teradyne's unique data domain including time-series parametric test data (STDF/TEMS), wafer map analysis, etc.
Drive applied research and model innovation, explore and evaluate new architectures, algorithms, and training methodologies, and translate promising approaches into production systems
Develop and maintain rigorous model evaluation frameworks, including validation methodologies, risk quantification, and production monitoring strategies
Lead technical design reviews
serve as final arbiter of ML architecture and modeling decisions for the team
Build and maintain production ML systems with a strong focus on reliability, scalability, and performance in Teradyne's ATE and manufacturing environments
Partner directly with customers and application engineers to understand real-world debug workflows and translate them into ML solutions
Mentor and develop junior ML engineers
cultivate a culture of technical rigor and continuous learning
Requirements
5+ years of experience in machine learning, applied AI, or related fields
Hands-on experience fine-tuning large language models
Experience with reinforcement learning (e.g., policy gradients, PPO, actor-critic methods)
Experience designing reward models or evaluation systems
Strong software engineering skills (Python, distributed systems familiarity)
Experience building production ML systems (MLOps, monitoring, deployment)
Ability to work cross-functionally with product, software, and hardware teams
Strong communication skills
comfortable engaging directly with customers & stakeholders
Computer vision skills in manufacturing inspection: defect detection, etc.
ML model deployment on edge devices is preferred, including quantization, ONNX, etc.
Open-source ML project contributions, publications, or technical patents is preferred
Knowledge of AI agent frameworks (LangChain, AutoGen) or reasoning-driven workflow design (Chain-of-Thought, Chain-of-Action) is preferred
Master's or Ph.D. in Computer Science, Electrical Engineering, Statistics, or a related quantitative field — or equivalent industry experience with a demonstrated record of technical innovation, including publications, open-source contributions, or patents
Nice to have
ML model deployment on edge devices is preferred, including quantization, ONNX, etc.
Open-source ML project contributions, publications, or technical patents is preferred
Knowledge of AI agent frameworks (LangChain, AutoGen) or reasoning-driven workflow design (Chain-of-Thought, Chain-of-Action) is preferred