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About This Role
Location Fremont, California, 94538 Category Sales JR105223 Automation 9050 Olympus Controls Corp. Regular Job Type Full time
Olympus Controls is looking for a technically curious, customer-facing Physical AI and Robotics Sales Specialist to help lead our next phase of growth in the Bay Area’s most advanced industries, including physical AI, robotics, high-tech OEM startups, semiconductor, and life sciences automation.
This person will sit at the intersection of robotics, automation, AI-enabled systems, and business development. The ideal candidate can walk into a conversation with an AI robotics startup, semiconductor equipment manufacturer, or advanced OEM and credibly discuss robot integration, machine vision, device communication, motion control, proof-of-concept development, and production automation challenges.
Role Overview
The High-Tech Robotics & Physical AI Specialist will work closely with Sales Engineers, Applications Engineers, supplier partners, and customers to identify, develop, demonstrate, and close advanced robotics opportunities. The role is highly customer-facing and requires a mix of technical credibility, sales drive, hands-on programming ability, and excitement for emerging automation technologies.
This person will focus heavily on Bay Area industries where robotics and AI are reshaping manufacturing and product development.
Key Responsibilities
- Own and grow strategic robotics and physical AI opportunities across the Bay Area.
- Partner with local Sales Engineers to identify target accounts, develop opportunity plans, and advance complex robotics projects from discovery through close.
- Build credibility with technical customers by understanding their automation challenges, system architecture, communication requirements, and integration constraints.
- Develop and deliver engaging product demonstrations, proof-of-concepts, and application mockups using robotics, vision, end-effectors, sensors, motion, and related automation technologies.
- Work with supplier partners to configure, quote, and position solutions for robotics, machine vision, sensing, motion, safety, and factory automation applications.
- Engage with major players in AI, robotics, semiconductor, electronics, and OEM markets with a consultative, technically intelligent approach.
- Support marketing events, technology days, customer visits, and supplier-led demonstrations.
- Stay current on emerging robotics and automation trends, including ROS-based development, robot programming, AI-enabled automation, machine vision, industrial communication protocols, and flexible manufacturing.
Ideal Technical Background
The strongest candidate does not need to be a deep software engineer but should be technically fluent enough to have credible conversations with advanced robotics and AI companies.
Ideal experience includes:
- Hands-on robotics programming or integration experience.
- Familiarity with ROS or ROS 2.
- Experience with script-based programming such as Python, JavaScript, C++, or similar.
- Understanding of industrial and device-level communication, such as Ethernet/IP, Modbus TCP, TCP/IP, IO-Link, serial communication, MQTT, OPC UA, or similar protocols.
- Ability to work with robots, machine vision, sensors, end-effectors, controllers, and peripheral automation devices.
- Understanding of how hardware and software come together in robotic systems.
- Ability to build demos, troubleshoot systems, and support proof-of-concept development.
- Experience working with electronics, semiconductor, OEM, robotics, or advanced manufacturing customers is highly valuable.
Sales and Customer Skills
This role requires someone who enjoys being in front of customers and can make complex technology exciting, understandable, and commercially relevant.
The ideal candidate will be able to:
- Ask strong discovery questions and uncover high-value automation opportunities.
- Translate technical customer needs into practical automation solutions.
- Present and demonstrate technology in an engaging, confident way.
- Coordinate across Sales Engineers, Applications Engineers, suppliers, and customers.
- Build trust with engineering, operations, manufacturing, and executive stakeholders.
- Balance hands-on technical work with disciplined sales follow-up.
- Prospect into emerging high-tech accounts and develop new business.
- Represent Olympus Controls as a credible automation partner for the Factory of the Future.
Qualifications
- Bachelor’s degree in Mechanical Engineering, Electrical Engineering, Software Engineering, Mechatronics, Robotics, Computer Science, or a related technical field preferred.
- Equivalent hands-on robotics, automation, or technical sales experience will also be considered.
- 2–5+ years of experience in robotics, automation, technical sales, applications engineering, systems integration, OEM equipment, semiconductor, electronics manufacturing, or a related technical field.
- Experience with collaborative robots, autonomous mobile robots, machine vision, sensing, motion control, or industrial automation platforms preferred.
- Comfortable with customer-facing work, demonstrations, proof-of-concepts, and technical presentations.
- Strong motivation to build something new, including ownership of a customer-facing robotics and AI lab.
The Right Fit
This is a great role for someone who gets excited about where robotics, AI, and automation are headed, but also understands how to turn that excitement into real customer projects. We are looking for someone who can be both technical and commercial. Someone who can build a demo in the morning, meet with a robotics startup in the afternoon, and help shape a strategic account plan the next day. The right person will help Olympus Controls become a stronger partner to the Bay Area’s most innovative companies in physical AI, robotics, electronics, semiconductor, and high-tech manufacturing.
In accordance with applicable wage transparency law requirements, the typical estimated total compensation for this position is $95,000 - $145,000 annually depending on experience, including potential bonus opportunities. Bonus amounts can be tied to company, location and/or individual performance, but no specific amount is guaranteed. All full-time associates are eligible for typical employment benefits expected from an industry leader (including Medical, Dental, Vision, 401K, life insurance, time off, employee assistance, etc.).
Founded in 1923, Applied Industrial Technologies (NYSE: AIT) is a leading value-added distributor and technical solutions provider of industrial motion, fluid power, flow control, automation technologies, and related maintenance supplies. Our leading brands, specialized services, and comprehensive knowledge serve MRO and OEM end users in virtually all industrial markets through our multi-channel capabilities that provide choice, convenience, and expertise.
Applied Industrial Technologies is built on a philosophy that puts people first. We are an equal opportunity employer, and we are committed to a workforce in which we enforce fair treatment and provide growth opportunities for everyone. All qualified applicants will receive consideration for employment regardless of age, race, color, national origin, genetics, religion, gender, marital status, physical or mental disability, or any other characteristic protected by applicable laws, regulations, and ordinances.
Salary Context
This $95K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At automation, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($120K) sits 43% below the category median. Disclosed range: $95K to $145K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
automation AI Hiring
automation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fremont, CA, US. Compensation range: $145K - $145K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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