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About This Role
About Standard Bots
Standard Bots is building the next generation of industrial robots \- hardware that's powerful, affordable, and designed to scale. We're the largest U.S. industrial robotics company by robots shipped, and our robots are showing up on factory floors everywhere from independent machine shops to NASA and Lockheed Martin.
Note: This role requires up to 50% domestic travel. That number goes down as we grow the team, but right now you're in the field \- a lot. If that's not for you, this probably isn't the right fit.
Also note: If you're looking for a role where you train models and push code, this isn't it. This is a mechanical \+ electrical \+ software trifecta that happens to use AI as a tool. The factory floor is your office.
The Role
The AI Applications Engineer sits at the intersection of robotics, AI, and hands\-on field work. You're not a pure software engineer and you're not a pure hardware engineer \- you're the person who can look at a manufacturing challenge, figure out how computer vision or a language model makes the robot smarter, and then actually build and deploy the solution.
You'll work closely with the sales, software and applications engineering teams to scope AI\-enabled solutions, build proofs of concept in our shop, and travel to customer sites to install and commission them. You'll own your projects end\-to\-end \- from initial design through field deployment and customer training.
This is a builder's role. Tinkerers and people who get bored at a desk are strongly encouraged to apply.
What You'll Do
AI Solution Design \& POC Development
\- Work with customers and the sales team to identify where AI \- computer vision, object detection, visual language models \- creates real leverage in their production process
- Design and build proofs of concept that validate the solution before anyone signs anything
- Develop documentation, sample code, and integration guides that help customers and internal teams replicate and scale what you've built
Field Deployment \& Integration
- Travel to customer sites to install, configure, and commission AI\-enabled robotic cells
- Own the full integration: camera placement and calibration, model deployment, I/O configuration, testing, and sign\-off
\- Troubleshoot on\-site when things don't go as planned \- you stay until it works
Customer Training \& Support
- Train customer operators and engineers on the AI capabilities of their Standard Bots deployment
- Be a trusted technical resource post\-install as customers push the system into new applications
- Feed real\-world deployment learnings back to our engineering and product teams
Internal Collaboration
- Work with the broader applications and engineering teams on new feature testing, peripheral integrations, and platform expansion
\- Help build the internal knowledge base on AI\-enabled applications \- what works, what doesn't, and why
What We're Looking For
Core Skills (Must have all or most)
\- Robot programming proficiency or adjacent software experience \- if you can write Python without an AI agent, you can probably program a robot having never touched one before
\- Prior robotics knowledge \- anything from high school robotics to professional manipulation experience; we care that you've actually worked with robots, not just read about them
\- Surface\-level fluency in ML models and techniques \- you can have a real conversation about convolutional neural networks, object detection, and visual language models without Googling the definitions mid\-sentence
\- Technical proficiency in an engineering environment \- you write documentation, manage your own projects, work well with a team, and don't need someone watching over your shoulder
\- Willingness to travel \- up to 50% domestically right now; that number will come down as we grow
Specializations (Must have at least one)
\- Mechanical Design \- pneumatics, camera mounts, end\-of\-arm tooling for manipulators; bonus points if you've designed a gripper
\- Electrical Design \- custom board fabrication, general electrical knowledge for wiring relays and solenoids in automation environments
\- Machine Vision \- you know which cameras and lenses to use for a given application and why; you've actually deployed a vision system, not just selected one on paper
Nice to Have
- Experience with collaborative robots (UR, Fanuc, KUKA, ABB, or similar)
- Hands\-on work with vision systems in a production or lab environment (Cognex, Basler, FLIR, or similar)
- Familiarity with ROS or other robotics middleware
- Background in a manufacturing or industrial automation environment
\- Experience at a startup \- you know what it means to build something from scratch with limited resources
Compensation and Benefits
The salary range for this role is $120,000 to $150,000, depending on experience. We are open to a variety of seniority levels for this role and will build compensation packages that are commensurate with seniority and skill level. Base salary is just one part of the overall compensation at Standard Bots. All Full\-Time Employees are eligible for Employee Stock Options. We also offer a package of benefits including paid time off, medical/dental/vision insurance, life insurance, disability insurance, and 401(k) to regular full\-time employees.
Compensation Range: $120K \- $150K
Salary Context
This $120K-$150K range is in the lower quartile 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 Standard Bots, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($135K) sits 37% below the category median. Disclosed range: $120K to $150K.
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.
Standard Bots AI Hiring
Standard Bots has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Glen Cove, NY, US. Compensation range: $150K - $150K.
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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