Interested in this AI/ML Engineer role at Anyware Robotics?
Apply Now →Skills & Technologies
About This Role
*
### About Anyware Robotics
Anyware Robotics builds general\-purpose mobile manipulator robots for industrial applications. Our robots are trusted by customers across logistics, retail, and manufacturing, supporting applications such as truck unloading, mobile palletizing, and machine tending.
### Description
### About Anyware
Anyware Robotics builds general\-purpose mobile manipulator robots for industrial applications. Our robots are trusted by customers across logistics, retail, and manufacturing, supporting applications such as truck unloading, mobile palletizing, and machine tending.
### The Role
The robots run on AnywareOS, our industrial physical intelligence system. Its architecture is modular and designed to generalize across applications without reprogramming. We are seeking a Senior AI Perception Engineer to advance Anyware's production 3D AI perception stack for warehouse automation robots, with a focus on object localization, pose estimation, scene reconstruction, and deployment on robot compute within application cycle\-time constraints.
This role is for an engineer who has taken AI perception from idea or prototype through production and real\-world operation. You will make pragmatic tradeoffs across research potential, accuracy, reliability, latency, compute, data, maintainability, engineering effort, and delivery timelines, while providing senior technical leadership through architecture decisions, design reviews, incident investigations, and mentoring.
### What you'll do
- Advance AI perception capabilities end to end within Anyware's existing perception stack—from problem definition and prototyping through deployment, monitoring, failure analysis, and production improvement.
- Develop and improve robust 3D perception algorithms and pipelines for object localization, pose estimation, scene reconstruction, and manipulation using RGB\-D and other sensing modalities.
- Make pragmatic engineering tradeoffs across accuracy, robustness, latency, compute cost, data availability, maintainability, engineering effort, and delivery timelines.
- Improve perception reliability on physical robots by diagnosing and correcting failures caused by difficult scenes, changing lighting, sensor noise, and differences between training data and customer environments.
- Strengthen production readiness through better data, evaluation, regression testing, release criteria, observability, and systematic field\-failure analysis.
- Collaborate across robotics teams to integrate perception reliably into planning, manipulation, controls, and system software workflows.
### Required Skills
- MS or PhD in Robotics, Computer Vision, Computer Science, or a related field.
- 3\+ years of professional experience developing robotic perception systems, with direct responsibility for deploying and operating perception software in production on physical robots.
- Strong Python / C\+\+ skills for developing real\-time robotic perception pipelines, including sensor\-data processing and model integration using ROS or ROS2\.
- Strong foundation in 3D geometry, camera models and calibration, RGB\-D or point\-cloud processing, and 3D object localization, segmentation, or pose estimation.
- Hands\-on experience developing AI perception models using PyTorch or similar frameworks and deploying them under latency and compute constraints on robot hardware.
- Experience building production data and evaluation pipelines, regression tests, observability, and systematic root\-cause analysis for field failures.
- Strong engineering judgment and senior\-level ownership of architecture and design decisions, balancing technical ambition with reliability, resources, maintainability, and delivery timelines.
### Nice to have
- Experience building data flywheels, hard\-example mining, offline evaluation, or model monitoring for deployed perception systems.
- Experience with multi\-sensor fusion and synchronization across cameras, LiDAR, encoders, or other robot sensors.
- Experience optimizing AI inference on edge GPUs using TensorRT, ONNX Runtime, CUDA, quantization, or similar techniques.
- Experience using Isaac Sim, synthetic data, or sim\-to\-real workflows for perception development and validation.
- Experience deploying robotic perception systems in warehouse, logistics, or other industrial environments.
### Why Anyware
- An A\+ Team: Anyware is built by top talents. Collectively, our team has won six Best Paper Awards and Finalists, including the ICRA 2025 Best Paper Award and ICRA 2024 Best Paper Award.
- An A\+ Robot: Our robot, Pixmo, won the Best Innovation Award (top\-1\) at both ProMat 2025 and MODEX 2026 — the two largest logistics automation exhibitions in the world. Pixmo also won the RBR50 Robotics Innovation Award.
- Real Impact: Your perception work does not stop at a demo. It ships into real customer sites, directly affects robot reliability and throughput, and must perform under real\-world variation.
- Fast Growth: You will have strong ownership and real responsibility in a growing robotics company.
- Generous Equity: We offer generous equity in addition to salary and benefits.
### Compensation
The expected salary range for this role is 150K\-240K USD/year, depending on experience and qualifications.
Additional compensation may include equity, benefits, or other incentives, where applicable.
### Benefits \& Perks
- Comprehensive health insurance for you and your family
- Flexible Paid Time Off (PTO)
- Paid sick leave
- 401(k) plan support
- Daily meal credit
### Equal Opportunity Statement
Anyware is an equal opportunity employer. We are committed to creating an inclusive workplace and welcome applicants from all backgrounds. Employment decisions are based on qualifications, merit, and business needs.
### How to Apply
Please submit your resume and any relevant materials through the Anyware Career platform. We look forward to learning more about your experience and interest in joining our team.
#### Salary
$150,000 \- $240,000 per year
Salary Context
This $150K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Anyware Robotics, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($195K) sits 11% below the category median. Disclosed range: $150K to $240K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Anyware Robotics AI Hiring
Anyware Robotics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fremont, CA, US. Compensation range: $240K - $240K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.