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About This Role
Senior AI Engineer @
Location: San Francisco(Hybrid) Experience: 8\+ years Team: AI Engineering
About Revi
Revi is a Series A restaurant commerce platform building the AI operating system for the restaurant industry. We're rapidly transforming how restaurants run their business through agentic AI — from voice agents that handle customer interactions to agentic commerce and intelligent automations across our ReviOS dashboard. We're a small, fast\-moving team where engineers ship to production daily and own meaningful surface area from day one.
The Role
We're hiring a Senior AI Engineer to architect, build, and own production\-grade agentic AI systems at Revi. You'll lead the design of single\-agent and multi\-agent systems that take real actions for restaurants and their customers, own the deterministic ML stack that powers personalization and decisioning across the product, and set the bar for how we deploy, monitor, and evolve AI in production. This is an IC role for a builder who has shipped — repeatedly — and knows what production reliability for AI systems actually requires.
What You'll Do
- Architect and ship agentic AI systems end\-to\-end — designing single\-agent and multi\-agent topologies, tool interfaces, memory and state management, evaluation harnesses, and the production infrastructure that holds it all together
- Own the deterministic ML modeling stack — feature pipelines, training, ranking/retrieval, evaluation, and online serving for high\-traffic use cases
- Drive integration with LLM providers (Claude, GPT, and others) at production scale — handling latency, cost, reliability, prompt iteration, structured outputs, function/tool calling, and provider failover
- Own production deployment and operations: CI/CD, containerization, observability, on\-call quality, cost monitoring, and graceful degradation strategies for AI systems
- Set technical direction and review architectural decisions across the AI Engineering team; mentor junior engineers and raise the engineering bar
- Partner with product and leadership to translate Revi's growth strategy into concrete AI roadmaps
What We're Looking For
Basic Qualifications
- 6\+ years of professional work experience building and shipping ML/AI systems in production. Personal projects, hackathons, and class work do not substitute for this requirement — we need engineers who have operated AI in real production environments at meaningful scale
- Deep, hands\-on experience with agentic AI — you have personally architected and shipped single\-agent or multi\-agent systems in a production setting. You can speak fluently about tool use, planning loops, evaluation, failure modes, and the tradeoffs between orchestration patterns
- Strong deterministic ML modeling background — production experience training and serving classification, ranking, retrieval, or recommendation models. Familiarity with the full ML lifecycle: data, features, training, evaluation, serving, monitoring
- Production experience with LLM providers (Anthropic Claude, OpenAI GPT, or equivalent) — not just API calls, but production integration including prompt engineering at scale, tool use, structured outputs, evaluation, cost control, and reliability engineering
- Production deployment experience is critical and non\-negotiable — you have personally owned services running in production, including CI/CD, containerization (Docker/Kubernetes), cloud infrastructure (AWS/GCP/Azure), monitoring/observability, and on\-call. Candidates without demonstrable production deployment ownership will not be considered
- Hands\-on production experience with large\-scale data processing tools — including distributed batch processing (Spark, Databricks, or equivalent) and streaming/event pipelines (Kafka, Kinesis, Pub/Sub, Flink, or equivalent). You have built and operated real data pipelines feeding ML/AI systems at scale, not just consumed pre\-processed datasets
- Excellent Python, strong fundamentals in distributed systems, data engineering, and modern cloud infrastructure
- BS, MS, or PhD in Computer Science, Machine Learning, or a related quantitative field — or equivalent professional track record
Preferred Qualifications
- Prior Staff or Tech Lead experience at a high\-growth startup or a top\-tier tech company
- Experience with voice AI, real\-time conversational systems, or speech\-to\-text/text\-to\-speech pipelines
- Deep experience with vector databases, retrieval systems, and large\-scale RAG architectures
- LLM observability and evaluation tooling expertise (LangSmith, Langfuse, custom eval frameworks)
- Experience in restaurant tech, commerce platforms, or B2B SaaS at scale
- Experience fine\-tuning, distilling, or post\-training open\-weight models for production
Why Revi
- Architect and own the AI systems powering a category\-defining restaurant commerce platform
- Direct partnership with leadership on technical strategy and product direction
- Ship to real customers at startup speed — meaningful equity and meaningful surface area
- Strong, AI\-forward engineering culture with high autonomy and high standards
Our Values
- Heart: A team that is passionate about what they do, with a heart of giving back.
- Impact: Being a versatile team player with an innovative mind and a firm backbone to make an impact on everything they touch.
- Excellence: A team committed to excellence in all we do, with integrity and supreme service.
Perks and Benefits of Joining the Revi Team
- Architect and own the AI systems powering a category\-defining restaurant commerce platform
- Direct partnership with leadership on technical strategy and product direction
- Ship to real customers at startup speed — meaningful equity and meaningful surface area
- Strong, AI\-forward engineering culture with high autonomy and high standards
- Competitive compensation, meaningful early\-stage equity, and a strong AI\-forward culture
- Excellent and comprehensive health plans (Medical, dental, vision, etc)
- Flexible Vacation Policy, Paid holidays
- Organized volunteer events to give back to our community
- Off\-sites, events and happy hours
- 401k
- Comp range \- Base : 220k USD \- 270k USD \+ Equity
Pay: $220,000\.00 \- $270,000\.00 per year
Work Location: In person
Salary Context
This $220K-$270K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At REVI, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($245K) sits 32% above the category median. Disclosed range: $220K to $270K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
REVI AI Hiring
REVI has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $170K - $270K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,258 tracked positions. That's 26% 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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