Machine Learning Engineering Manager (Teradyne, North Reading, MA)

Teradyne•North Reading, MA

4d•$199,600 - $319,300•Onsite

About The Position

Teradyne is a global leader in automated test equipment and industrial automation. Our technologies support advanced semiconductor and electronics testing, helping manufacturers bring products to market faster and with greater confidence. As we deepen our AI capabilities, we’re looking for an engineering leader who combines strong technical judgment with the ability to grow a team. As Technical Lead Manager, AI Engineering, you will be the technical authority for one of Teradyne’s AI Product tracks and manage the engineers who deliver it. This is a technical leadership role first: you will set architecture and modeling direction, lead design reviews, and stay close enough to the work to make difficult technical decisions yourself. You will manage a team of approximately five to ten ML engineers, own a defined portion of the multi-year AI product roadmap, and move initiatives from evaluation through benefit realization. You will report to the Head of Machine Learning and partner closely with business unit stakeholders.

Requirements

8+ years of experience in machine learning, applied AI, or a related field, including production ownership of ML systems.
Experience leading engineers through mentoring, technical leadership, or management.
Depth in at least one of the following: time-series and anomaly detection; computer vision for inspection; or applied LLM and agentic systems.
Experience deploying and maintaining ML systems, including MLOps, monitoring, evaluation, and iteration.
Ability to establish technical direction and communicate effectively with customers, business units, and executives.
Master’s or Ph.D. in Computer Science, Electrical Engineering, Statistics, or a related quantitative field—or equivalent industry experience demonstrating technical leadership.

Nice To Haves

Experience hiring and building an engineering team in a competitive market.
Experience in semiconductor, automated test equipment, or manufacturing environments.
ML and deep learning: PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn; supervised, self-supervised, and reinforcement learning.
Time-series: Forecasting, prediction, and anomaly detection using high-volume parametric test data, including STDF/TEMS.
MLOps: MLflow, Weights & Biases, Hugging Face, model registries, ML CI/CD, and production monitoring.
Data engineering and infrastructure: Spark, Pandas, SQL, Docker, Kubernetes, Azure ML, and GCP Vertex AI.
LLMs: Fine-tuning (SFT, RLHF, DPO), prompt engineering, evaluation frameworks, and agentic workflow design.

Responsibilities

Set modeling and architecture standards for your track, lead design reviews, and make final technical decisions for your team’s ML systems.
Stay hands-on by prototyping to reduce risk, reviewing data, code, and models, and maintaining a high engineering bar.
Lead initiatives through evaluation, feasibility, development, pilot, and benefit realization; own the associated FTE estimates and delivery commitments.
Hire, coach, and develop ML engineers, including performance management and career growth.
Translate business unit needs into tractable ML problems and explain model behavior in such a way that stakeholders can act on.
Partner with peer Technical Lead Managers to maintain consistent standards across the AI Product organization.
Own production quality, including monitoring, drift, retraining, and response to model issues in customer environments.
Represent your track in roadmap, resourcing, and executive reviews, including annual FTE and headcount planning.