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About This Role
»* Date: Oct 2, 2026
Location: North Reading, MA, US
Company: Teradyne
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.
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.
Opportunity Overview
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.
Key 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 terms 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.
All About You
You are an experienced ML practitioner who has taken systems into production and remained accountable for their performance. You can set and defend technical direction with senior stakeholders, make sound trade-offs, and identify work that is unlikely to deliver value. You also enjoy helping engineers grow—whether you have managed a team formally or are a strong technical lead ready to take that step.
Required Qualifications
- 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.
Preferred Qualifications
- Experience hiring and building an engineering team in a competitive market.
- Experience in semiconductor, automated test equipment, or manufacturing environments.
Relevant Technical Experience
- 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.
Compensation: The base salary range for this role is $199,600-$319,300. 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.
Job Segment: Computer Science, Engineer, Electrical Engineering, Engineering Manager, Database, Technology, Engineering
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Salary Context
This $199K-$319K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Teradyne, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($259K) sits 22% above the category median. Disclosed range: $199K to $319K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Teradyne AI Hiring
Teradyne has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span North Reading, MA, US, Deer Park, IL, North Reading, MA. Compensation range: $258K - $319K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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