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Date: Aug 4, 2026
Location: North Reading, MA, US
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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, 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
We're seeking an exceptional Senior AI/ML Engineer to join our advanced Physical AI engineering team at the forefront of industrial robotic innovation. This role sits at the intersection of cutting\-edge AI technology and real\-world product deployment, where you'll help define what's possible in physical AI for both autonomous mobile robots (AMRs) and industrial robotic arms.
Our team is building differentiating solutions that push the boundaries of what robots can perceive, understand, and interact with in dynamic real\-world environments. You'll work alongside world\-class engineers to deliver full\-stack AI solutions that don't just work in simulation—they thrive in our customers' facilities.
Teradyne Robotics is transforming industrial automation through intelligent robotic solutions. Our portfolio includes Universal Robots (UR), the global leader in collaborative cobots, and Mobile Industrial Robots (MiR), pioneers in autonomous mobile robots. Together, we're building the future of flexible, intelligent automation for manufacturing and logistics environments worldwide.
You'll lead the development and deployment of physical AI capabilities that enable robots to navigate, manipulate, and interact with their environments with unprecedented intelligence. This includes advancing our work in obstacle detection, object identification and classification, pose estimation, SLAM technologies, and precision localization systems.
Working across our platform for simulation and data generation, you'll help engineer solutions that leverage diverse sensor modalities—from RGBD cameras and LiDAR to Time\-of\-Flight sensors—making informed trade\-offs based on performance, cost, and application requirements. You'll be responsible for taking AI innovations from concept through production deployment and ongoing customer support.
As a senior member of the team, you'll collaborate closely with R\&D teams at Universal Robots and Mobile Industrial Robots, helping shape the future of collaborative robotics and autonomous mobile platforms across the Teradyne Robotics portfolio.
Critical to this role: You must approach your work with scientific rigor. This means deeply understanding the performance characteristics, quality metrics, and limitations of every model you build. You'll need to systematically analyze how successive training runs impact these qualities, maintaining clear visibility into what's improving, what's regressing, and why. We build solutions our customers trust, and that trust is earned through rigorous validation and honest assessment of capabilities and boundaries.
All About You
Deep understanding of sensor technologies (RGBD, LiDAR, ToF, etc.) and the ability to architect solutions that leverage their respective strengths and limitations
Experience shipping AI\-powered products to market and supporting them in production customer environments
Good software engineering fundamentals with experience integrating AI/ML capabilities into existing robotics software stacks
Expertise in data science and data engineering—you understand that great AI solutions are built on great data
Collaborative mindset with experience working in cross\-functional teams building complete systems, not just isolated algorithms
Demonstrated ability to rigorously evaluate ML model performance, establish meaningful metrics, and track quality across training iterations
Experience utilizing simulation environments to accelerate engineering and validation workflows
Technical Expertise:
Core robotics AI concepts: obstacle detection, object identification and classification, pose estimation
Various SLAM approaches and their trade\-offs
Localization techniques from precision to relative positioning
Experience working with both robotic manipulators and mobile platforms
Hands\-on experience with NVIDIA Isaac platform and tools
Background working with AMRs or industrial robotic arms in production settings
Knowledge of and interest in the emerging field of Vision\-Language\-Action (VLA) models and their application to robotics
Contributions to robotics or AI research communities
Why This Role Matters
You'll be joining a team that's already delivered mature platforms for simulation and data generation across multiple problem domains. We're not just building technology for tomorrow—we're deploying it today, solving real problems for customers in industrial automation while continuously pushing the state of the art. At Teradyne Robotics, you'll have the unique opportunity to impact both mobile robotics and collaborative manipulation platforms used by thousands of customers worldwide.
If you're energized by the challenge of making advanced AI work reliably in the physical world, we want to hear from you.
This is an onsite position with remote flexibility. Some travel will be required.
Compensation:
The base salary range for this role is $219,800\-$351,700\. 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: Test Engineer, Testing, Developer, Software Engineer, Manufacturing Engineer, Engineering, Technology
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Salary Context
This $219K-$351K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($285K) sits 33% above the category median. Disclosed range: $219K to $351K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Teradyne AI Hiring
Teradyne has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in North Reading, MA, US. Compensation range: $186K - $351K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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