Senior Software Machine Learning Engineer

Teradyne•North Reading, MA

2d•$141,300 - $242,000

About The Position

Teradyne is seeking a senior individual-contributor machine learning engineer to provide technical leadership across the company’s AI initiatives. The role owns the technical direction and end-to-end quality of machine learning systems, from research and experimentation through production deployment and ongoing performance monitoring. The engineer will set modeling and engineering standards, lead technical design reviews, serve as the final authority on ML architecture and modeling decisions, and take ownership of complex, high-impact problems. The work includes time-series analysis of semiconductor parametric test data, wafer map analysis, computer vision for defect detection, adaptive test optimization, and applied LLM systems. The engineer will develop production systems for Teradyne’s ATE and manufacturing environments, collaborate with customers and application engineers to address real-world debug workflows, and mentor junior ML engineers while encouraging technical rigor and continuous learning.

Requirements

At least 5 years of experience in machine learning, applied AI, or a related field.
Hands-on experience fine-tuning large language models.
Experience with reinforcement learning, such as policy gradients, PPO, or actor-critic methods.
Experience designing reward models or evaluation systems.
Strong software engineering skills, including Python and familiarity with distributed systems.
Experience building production ML systems, including MLOps, monitoring, and deployment.
Ability to work cross-functionally with product, software, and hardware teams.
Strong communication skills and comfort engaging directly with customers and stakeholders.
Computer vision skills in manufacturing inspection, including defect detection.

Nice To Haves

Experience deploying ML models on edge devices, including quantization and ONNX.
Contributions to open-source ML projects, publications, or technical patents.
Knowledge of AI agent frameworks such as LangChain or AutoGen, or reasoning-driven workflow design such as Chain-of-Thought or Chain-of-Action.

Responsibilities

Own the technical direction and quality of machine learning model development across Teradyne’s AI initiatives, setting and enforcing team engineering standards.
Lead end-to-end development of production ML systems, including data ingestion, feature engineering, model architecture, training pipelines, evaluation frameworks, and deployment.
Design and implement ML approaches for Teradyne’s data domains, including time-series parametric test data (STDF/TEMS) and wafer map analysis.
Drive applied research and model innovation; evaluate architectures, algorithms, and training methodologies and translate promising approaches into production systems.
Develop and maintain model evaluation frameworks, including validation methodologies, risk quantification, and production monitoring strategies.
Lead technical design reviews and make final decisions on ML architecture and modeling for the team.
Build and maintain reliable, scalable, high-performance production ML systems for ATE and manufacturing environments.
Work with customers and application engineers to understand debug workflows and translate them into ML solutions.
Mentor and develop junior ML engineers and foster technical rigor and continuous learning.