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About This Role
Forward Deployed Engineer \- AI Partnerships (Teradyne Robotics, San Jose, CA)
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ID: 11747
Function: Engineering
Location:
San Jose, CA, US
At Universal Robots, part of Teradyne Inc., Our vision is to create a world where people work with robots, not like robots. And as the market leader with 75,000\+ collaborative robots (cobots) already installed worldwide, we’re well on the way to achieving it. We employ 1000\+ people in offices across North America, South America, Europe and Asia and we’re growing all the time. Our team is made up smart, creative people working at the forefront of automation. Together we find innovative solutions to some of the most important manufacturing issues facing businesses today. We dare to do what others find impossible\- working with advanced technologies to change in the way businesses operate, So if you’re looking to build your career with a ground\-breaking technology company in dynamic environment with career advancement UR is the place for you.
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. Opportunity
Overview
We are seeking a Forward Deployed Engineer (FDE) to join our AI Partnerships team in the San Francisco Bay Area. This role sits at the intersection of robotics platforms, partner integration, and physical AI deployment.
You will work directly with leading AI model builders, robotics startups, and strategic partners to help them build, validate, and deploy applications on Teradyne’s robotics platforms, including Universal Robots (UR) and Mobile Industrial Robots (MiR).
Unlike traditional robotics or AI roles, this position is deeply partner\-facing and deployment\-oriented. Your primary responsibility is to ensure partners can successfully leverage our robotics platform stack (control, motion, interfaces, Polyscope integration) to bring their products to life in real\-world environments. While you will collaborate closely with AI/ML teams, your focus will be on robotics systems, interfaces, and performance in production environments.
What You’ll Do
Partner\-Facing Deployment \& Integration
- Work hands\-on with strategic partners to integrate their applications onto UR and MiR platforms
- Serve as the technical bridge between partner engineering teams and internal robotics/product teams
- Debug and resolve issues across software, control, and system integration layers in real\-world deployments
- Support partner teams onsite when needed during critical development and validation phases
Robotics Platform Engineering
- Enable partner success across key platform layers:
+ Motion planning and optimization
+ Control interfaces and trajectory execution
+ Polyscope and URCap integration
+ Robot APIs, SDKs, and communication interfaces
- Develop and maintain reference implementations, integration examples, and tooling for partners
- Contribute feedback to internal product teams based on real\-world partner usage patterns
Software Development \& Systems Integration
- Build and adapt software components using Python, ROS2, and Linux\-based systems
- Integrate partner software stacks with robot systems using standard and custom interfaces
- Support debugging of latency, control loops, and system\-level performance issues
- Assist with simulation, testing, and deployment pipelines where relevant
AI \& Physical AI Collaboration (Supportive)
- Work alongside AI/ML teams to ensure models interface effectively with robotic systems
- Understand and support integration of perception, VLA, or learning\-based components into robot workflows
- Help bridge sim\-to\-real gaps from a robotics execution standpoint (e.g., control, timing, actuation)
All About You
We seek individuals who share our passion and determination. Our commitment to customer success drives us to go the extra mile. If you’re ready to join us in this mission, take a closer look at the minimum criteria for the position.
Required Qualifications
- BS/MS in Robotics, Mechatronics, Computer Science, or related field
- 4\+ years of experience in robotics engineering or systems integration roles
- Strong hands\-on experience with:
+ ROS2 and Linux\-based robotics systems
+ Python development (production\-level codebases)
+ Robot control, kinematics, motion planning, and system integration
- Experience working directly with robot hardware (integration, debugging, bring\-up)
- Ability to troubleshoot across hardware/software/system boundaries in real deployments
- Strong communication skills with the ability to work directly with external partners/customers
Preferred Qualifications
- Experience with industrial robot platforms (UR preferred) or similar systems
- Familiarity with:
+ Robot programming environments (e.g., URScript, PLCs)
+ Real\-time control systems and motion optimization
+ Perception pipelines and sensor integration (RGBD, LiDAR, etc.)
- Exposure to physical AI concepts (imitation learning, RL, or VLA models)
- Experience with simulation environments (e.g., NVIDIA Isaac Sim)
- Background working in startup, partner\-facing, or customer\-facing engineering roles
Who You Are
- A systems thinker who can move fluidly between software, control, and hardware
- Highly hands\-on, comfortable working on robots in real\-world environments
- Comfortable in ambiguous, fast\-moving partner environments
- Strong bias toward execution and problem\-solving in production contexts
- Able to translate between research, product, and deployment constraints
Why This Role
- Work directly with the most advanced AI model builders and robotics startups globally
- Play a critical role in shaping how physical AI systems get deployed in the real world
- Influence both partner success and the evolution of our robotics platform stack
- Operate at the cutting edge of robotics \+ AI commercialization
Compensation: The base salary range for this role is $125,700 \- $201,200\. 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.
Nearest Major Market: San Jose
Nearest Secondary Market: Palo Alto
Job Segment: Testing, Computer Science, Engineer, Linux, Manufacturing Engineer, Technology, Engineering
Salary Context
This $125K-$201K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($163K) sits 10% below the category median. Disclosed range: $125K to $201K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Teradyne AI Hiring
Teradyne has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Jose, CA, US, North Reading, MA, US. Compensation range: $186K - $271K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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