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About This Role
Everyone's building AI agents, but almost nobody gets them to production.
Building an impressive demo is easy. Building an AI agent that can securely take action inside enterprise systems is hard. The moment an agent accesses customer data, executes a workflow, or makes changes on behalf of a user, authorization, governance, and trust become the real engineering challenge.
Arcade is the MCP runtime that gives agents the power to do both seamlessly. We connect agents to the systems they act in, then give each one a permission slip and a paper trail \- proof of what it's allowed to do, and a record of what it did. That's what makes AI safe to turn loose: real actions, on real systems, already shipping inside Fortune 100 companies.
The Revolution Needs You
Every AI app needs agentic tools that let AI models take real actions. Without tools, AI can only chat. With tools, AI can actually do things. We're building the definitive tools catalog, actions platform, and governance model that will unlock AI's true potential. Think Zapier for AI Actions. Think Auth0 for AI. Think really big.
Why This Is The Opportunity of a Lifetime
- Founder\-Market Fit : Our CEO previously founded Stormpath (acquired by Okta), where he Traction: Real deployments with Fortune\-100 customers like Morgan Stanley and Open Table
- Founder\-Market Fit: Our CEO previously founded Stormpath (acquired by Okta), where he created the first Authentication API for developers. He's done this before \- and this time the market is 10x bigger. Our CTO led the vector database team at Redis, shipped 100\+ LLM applications, and is a contributor to LangChain and LlamaIndex. He knows this space better than anyone.
- Dream Team: We've assembled authentication, integrations, distributed systems, and AI experts from Okta, Redis, Microsoft, Splunk, Ngrok, Google, Airbyte, Disney, and HPE who've built and founded multiple successful developer platforms.
- Perfect Timing: Every enterprise is racing to put agents in production \- almost none get there. The problem isn't better models, it's proving which agent can take which action, on behalf of which user, against which system. That's us.
- Massive Market : We're building critical infrastructure for the biggest technological shift of our generation. Every AI app will need what we're building.
- Backed By The Best: Our Series A round is led by SYN Ventures, with strategic investment from Morgan Stanley and Wipro. Our earlier investors have also backed Databricks, Clickhouse, MongoDB, Perplexity, Cohere, ScaleAI, Confluent, Elastic, and Firebase. They see what we see \- this is going to be huge.
The Challenge
You'll report to the Engineering Manager for Tools and Growth. The Tools team owns Arcade's tool catalog — thousands of tools across many services, growing faster than any human can review by hand. These toolkits are what let agents actually do things in the real world, and keeping them broad, reliable, and high\-quality is the work that makes Arcade useful.
As a Software Engineer on Tools, you'll build and maintain the toolkits agents depend on: take a vendor's API, design a clean set of tools on top of it, write the integration code, test it against real API behavior, and keep it healthy as those APIs change underneath us. You'll work close to the agent itself — how a model reads a tool description, how it decides which tool to call, and whether the result comes back in a shape the agent can use. You'll be doing all of this by building our own agentic tools to automate as much of this as possible.
The team is early and the catalog is growing fast, so you'll have real ownership from day one and a direct hand in the patterns every toolkit author after you inherits.
What You'll Do
- Build the agents we use internally to build, maintain, and scale our toolkits and skills.
- Build new toolkits — take a vendor's API and turn it into a high\-quality set of tools that agents can call reliably.
- Maintain and improve the existing catalog — fix breakages, sharpen tool descriptions and response shapes, and keep quality high as upstream APIs change.
- Write tests and evals that prove a tool does what it claims — against real API behavior and across different models.
- Treat LLMs and agents as a first\-class part of the job — tune tool descriptions, tool\-use behavior, and how an agent actually calls what you built.
- Contribute to the shared patterns and tooling that make building the next toolkit faster.
- Engage with and push the MCP ecosystem, where the standards for agent tools are still taking shape.
Required Skills
- 4\+ years of software engineering experience shipping production code.
- Strong Python and Typescript.
- Experience building and consuming APIs — REST, auth flows, pagination, rate limits, and the messy parts of real\-world integrations.
- A testing mindset — you write tests that catch real failures, not just green checkmarks.
- Comfort with ambiguity — early team, a narrow charter that will expand, decisions made with incomplete data.
- An insatiable desire to ship.
Bonus Points
- LLM application experience — prompting, retrieval, tool use, or agent design.
- Familiarity with the MCP (Model Context Protocol) ecosystem or similar agent\-tool protocols — extra bonus if you've filed an issue against the spec.
- You've built evals or measurement systems for ML/AI behavior.
- Prior experience at an API platform, integrations\-heavy product, or developer tools company.
- Open\-source contributions.
- Experience at an early\-stage startup, and you loved it.
Join The Movement
We're not just building a product \- we're leading a movement to transform AI from just chatbots to agents that can take actions against real systems. This is your chance to be at the forefront of that revolution.
If you want to look back in 5 years and say, "I helped build that", then we want to talk to you. Ready to make AI actually useful? Apply Now
Compensation and Benefits
This role offers a competitive salary, equity, and benefits. Compensation is aligned with the range below and determined based on a candidate's background, experience, and performance.
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 Arcade Food Theatre, 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.
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.
Arcade Food Theatre AI Hiring
Arcade Food Theatre has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
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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