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About This Role
We're hiring a Junior AI Engineer to help us design, build, and ship agentic AI systems and ML\-powered features into production. You'll work alongside senior engineers on real problems — building agents that take actions on behalf of restaurants, tuning models that personalize customer experiences, and integrating frontier LLMs into our product stack. This is a hands\-on builder role; expect to write production code, deploy services, and iterate quickly based on user feedback.
What You'll Do
Build and ship agentic AI features — single\-agent and multi\-agent systems that perform real tasks for restaurants and their customers
Develop deterministic ML models (classification, ranking, regression, retrieval) for use cases across the platform
Integrate LLM APIs (Anthropic Claude, OpenAI GPT, and others) into product workflows, including prompt engineering, tool use, structured outputs, and retrieval\-augmented generation
Deploy and maintain AI/ML services in production — containerized, observable, and reliable
Work closely with senior engineers and product to translate fuzzy product ideas into shipped features
Contribute to evaluation and monitoring frameworks so we know our agents and models actually work
What We're Looking For
Basic Qualifications
0–3 years of experience building AI/ML systems. This can come from full\-time work, internships, or substantive class/research projects — what matters is depth, not the source
Demonstrable hands\-on experience with agentic AI — single\-agent or multi\-agent systems built using frameworks like LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, or custom orchestration
Working knowledge of deterministic ML modeling — building, training, and evaluating models with libraries like scikit\-learn, XGBoost, LightGBM, or PyTorch
Hands\-on experience integrating LLM APIs (Claude, GPT, Gemini, etc.) into a working application — including prompt design, tool calling, and handling streaming/structured responses
Some deployment experience — you've put a service behind an API, containerized it, and run it somewhere real (AWS/GCP/Azure, Modal, Render, Fly, etc.)
Strong Python skills; comfort with Git, REST APIs, and basic SQL
BS or MS in Computer Science, Machine Learning, or a related quantitative field — or equivalent demonstrable skill
Preferred Qualifications
Experience with vector databases (Pinecone, Weaviate, pgvector, etc.) and RAG pipelines
Familiarity with LLM observability/eval tools (LangSmith, Langfuse, Braintrust)
Exposure to distributed data processing (Spark, Databricks) or streaming systems (Kafka, Kinesis, Pub/Sub) — even from an internship or coursework
Exposure to voice AI, speech\-to\-text, or real\-time conversational systems
Experience with restaurant tech, commerce platforms, or B2B SaaS
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
Ship real agentic AI to real customers — not demos
Work directly with senior engineers and leadership on architecture and product decisions
Move fast, own outcomes, and grow rapidly into a senior IC
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 : 130k USD \- 170k USD \+ Equity
Pay: $130,000\.00 \- $170,000\.00 per year
Work Location: In person
Salary Context
This $130K-$170K range is below the median 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. Entry-level AI roles across all categories have a median of $97,760. This role's midpoint ($150K) sits 19% below the category median. Disclosed range: $130K to $170K.
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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