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 Customer Facing Applied AI Engineer to help turn our computer vision and data platform into customer value across new and existing retail deployments. This role sits at the intersection of Computer Vision, Data, Product, Customer Success, and Field Operations. You will configure, tune, validate, and troubleshoot Simbe's AI pipelines for customer environments, helping retailers get accurate and actionable insights from Tally as quickly and reliably as possible.
### Why This Role Is High Impact
- You will be close to the customer and close to the technology, with direct influence on customer outcomes.
- You will translate real world deployment issues into repeatable product, pipeline, and model improvements.
- You will help Simbe scale across diverse store formats, fixture types, product categories, and retailer operating models.
### Responsibilities
- Lead customer specific AI configuration. Configure and tune Simbe's computer vision and data pipelines for new customers, new store formats, and expanded robot deployments.
- Improve customer accuracy. Investigate and resolve issues related to out of stocks, price tags, promo tags, top stock, product association, fixture detection, shelf coverage, and data quality.
- Support launches and expansions. Partner with Sales, Implementation, Product, Customer Success, Data, Computer Vision, and Field teams to ensure new deployments meet customer requirements and scale smoothly.
- Own release validation. Test customer facing model and pipeline updates, evaluate accuracy impacts, and help manage deployment readiness for releases tied to customer needs.
- Analyze production data. Use Python, pandas, SQL, internal tools, logs, robot imagery, and customer outputs to diagnose problems, quantify impact, and recommend fixes.
- Build operational tooling. Create or improve internal tools that reduce deployment time, automate QA, expose data quality issues, and help customer facing teams move faster.
- Close the loop with Product and Engineering. Turn repeated customer issues into clear product requirements, engineering tickets, and model improvement opportunities.
### Required Qualifications
- 3\+ years of experience in software engineering, applied machine learning, data engineering, computer vision operations, customer solutions engineering, or a related technical role.
- Strong Python skills and comfort working with data using pandas, notebooks, SQL, spreadsheets, or similar tools.
- Ability to troubleshoot complex technical issues across data pipelines, configurations, model outputs, logs, and customer reports.
- Comfort with Linux, command line workflows, Git, and production debugging.
- Strong analytical judgment, attention to detail, and ownership of customer outcomes.
- Ability to communicate clearly with technical and non technical stakeholders, including customer facing teams.
- Ability to prioritize effectively in a fast moving environment with evolving customer needs.
### Bonus Qualifications
- Experience with computer vision, machine learning evaluation, model training, annotation workflows, or neural network outputs.
- Experience in retail technology, robotics, IoT, data products, enterprise SaaS, or customer implementation roles.
- Experience with dashboards, internal web tools, data validation, QA automation, or release management.
- Experience working directly with customer success, implementation, product, or sales teams.
- Familiarity with image data, OCR, object detection, product matching, or sensor driven systems.
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 $120K-$160K range is below 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($140K) sits 35% below the category median. Disclosed range: $120K to $160K.
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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