Interested in this AI/ML Engineer role at Simbe Robotics?
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About This Role
Simbe is building the AI powered operating system for physical retail. Our autonomous robots and multimodal computer vision platform turn complex, constantly changing stores into accurate, actionable intelligence for leading retailers around the world. Simbe combines robotics, computer vision, machine learning, data infrastructure, and customer focused product design to help retailers improve shelf availability, price and promo execution, inventory accuracy, and store team productivity.
Simbe is looking for a Senior Computer Vision / Applied AI Engineer to build production AI systems that turn store imagery into trusted retail intelligence. This role will work across dense product detection, price tag and promo tag detection, OCR, barcode decoding, product association, segmentation, visual search, model evaluation, and customer specific model improvements. The right person isa strong ML engineer, an exceptional software engineer, and a practical builder who enjoys messy real world data, rapid iteration, and measurable customer impact.
### Why This Role Is High Impact
- You will work on dense, cluttered, real world visual scenes where accuracy directly drives customer value.
- You will help improve core Simbe use cases such as out of stock detection, price accuracy, promo compliance, top stock, and product location intelligence.
- You will work on the full model lifecycle: data, annotation quality, training, evaluation, release, monitoring, and production debugging.
### Responsibilities
- Build production CV models. Design, train, validate, and deploy models for object detection, segmentation, OCR, barcode localization, product recognition, shelf understanding, and other customer facing computer vision tasks.
- Own dataset quality. Curate, clean, version, and analyze large real world training datasets, including hard negative mining, annotation QA, data audits, and active learning workflows.
- Improve model performance. Research and implement model architecture, loss function, data augmentation, synthetic data, and evaluation improvements that increase precision, recall, latency, and robustness across customers.
- Support model releases. Contribute to model validation, release gates, inference wrappers, ONNX/TensorRT exports, and production monitoring so models are reliable in customer environments.
- Develop tooling. Build internal tools for model evaluation, annotation review, error mining, data visualization, dataset exports, and deployment readiness.
- Partner cross functionally. Work closely with Product, Customer Success, Data, Robotics Software, and Field Operations to turn customer issues into model and pipeline improvements.
- Stay current. Track modern work in open vocabulary detection, promptable segmentation, multimodal product understanding, visual search, OCR, and edge inference, and evaluate where it can create value for Simbe.
### Required Qualifications
- 5\+ years of experience in computer vision, applied machine learning, robotics perception, or related production AI systems.
- Strong Python experience and hands on experience with PyTorch or TensorFlow.
- Experience training and evaluating object detection, segmentation, OCR, visual search, or image recognition models.
- Strong understanding of dataset curation, annotation quality, model evaluation, error analysis, and experimentation.
- Experience building maintainable production code and data pipelines in a Linux based environment.
- Ability to balance research quality with production constraints such as latency, compute, memory, robustness, and ease of deployment.
- Strong communication skills and a customer value mindset.
### Bonus Qualifications
- Experience with retail, product recognition, shelf intelligence, OCR, barcode decoding, fine grained recognition, or visual product search.
- Experience with ONNX, TensorRT, CUDA, quantization, model profiling, or edge deployment.
- Experience with C\+\+, ROS/ROS2, RGBD cameras, 3D geometry, homography, calibration, or robotic perception.
- Experience with FiftyOne, CVAT, Labelbox, Roboflow, or other data centric ML tools.
- Experience with synthetic data generation, simulation, active learning, or automated annotation workflows.
- Publications, patents, open source contributions, or production systems in relevant CV/AI domains.
The base salary offered is based on market location and may vary depending on individualized factors for job candidates, including job related knowledge, skills, experience, and other objective business considerations. Subject to those same considerations, the total compensation package for this position may also include equity compensation, in addition to a full range of medical, financial, and other benefits. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
Simbe Values: R. E. T. A. I. L.
- Result Driven \- We are customer centric and results driven. We strive to create immense value for our team, partners, customers, and investors.
- Empathetic \- We are sensitive and mindful. We support each other in challenging times, both professionally and personally.
- Transparent \- We value open communication internally, and with our partners and customers. We are receptive to feedback.
- Agile \- We are eager to learn and adapt quickly to changes and customer needs.
- Innovative \- We are bold and innovative, with an intense focus on product design, user experience, and customer value.
- Leaders \- We strive for excellence. We are accountable, the best at what we do, and leaders in our field.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $160K-$200K range is above the median 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 Simbe 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 $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 ($180K) sits 16% below the category median. Disclosed range: $160K to $200K.
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.
Simbe Robotics AI Hiring
Simbe Robotics has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco Bay Area, CA, US. Compensation range: $160K - $200K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national 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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