Interested in this AI/ML Engineer role at Arcade Food Theatre?
Apply Now →Skills & Technologies
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" - special functions 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 and tool-calling platform 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
- 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
Arcade is hiring the principal machine learning engineer that wants to build the future of agent capabilities.
These core problems define the role:
- What’s old is new: How can a classic two-tower e-commerce recommendation system be used to make agents smarter? How can a classifier be used to make agents more efficient? The ability to take what’s been done and apply it to a new and ever-changing field is paramount for this role. We’ve launched Tool Recommendations as our first foray into agent–led recommendations, and there is a long list of features we want to create on these (& similar) building blocks.
- Own the models: Own the model pipeline from data ingestion to model publishing lifecycle - including models across embedding, reranking, classification, recognition, and other tasks. Be the leader of a group making agent capabilities, not just API calls, but intelligent and efficient abilities that outpace the competition. Provide insight and experience bringing ideas and practices that should be implemented in a stable but young model pipeline.
- Evaluation Obsession: Be willing to stand by your models because you’ve been provided able data to cover every possible outcome you can. Stand by the strengths and be readily willing to admit the faults so the team can be prepared. Be willing to go row by row in a spreadsheet without pride. That kind of activity is “beneath” you because this space is too new to claim any ego.
- Enterprise ready: Everything you build has to run where our customers run: their cloud, their hardware, and sometimes an air-gapped network. Quantization, export, serving, and packaging are part of the model design from day one, not a deployment step at the end.
Our largest customers run Arcade inside their own walls. A Fortune 100 bank doesn't send its agents' tool calls to someone else's API, and its agents can't wait on a frontier model to pick the right tool out of thousands. So the models behind Arcade's agentic features have to be small, fast, accurate, and shippable into a customer's VPC.
This is just one example of why custom, small models are the unlock to many of Arcade’s future products. You'll report directly to the Head of Engineering and own the models that sit in the runtime path of every agent call we serve.
Our first model for agent recommendation is already built (& patent pending), along with its training pipeline, but it needs to be productionized. You'll own it, decide what the ML stack at Arcade looks like, and set the patterns going forward. You’ll own the build-buy decisions for our stack going forward and have a healthy budget to spend.
This role is about shipping. While we're happy to publish what we learn, delivering the product to customers comes first. If you want six months in a notebook before anything reaches production, this isn't the right role. If you want to ship the model and the writeup in the same quarter, it is!
What You'll Do
- Own the training pipeline: Run Arcade's ML pipeline end to end, from data and training through evaluation and release. Make it reliable enough that shipping a new model is routine, not an event.
- Build the models: Fine-tune and train models for tool selection, routing, retrieval, and agent memory. Use whatever gets the job done distillation, embeddings, rerankers and more.
- Expand the use cases: Take our model into new territory, starting with search and recommendation over tools and agent context.
- Measure what matters: Build the eval system. That means offline evals against real agent traces, online measurement in production, and head-to-head comparisons with Claude, GPT, and Gemini, so every model decision has data behind it.
- Ship on-prem: Quantize, optimize, and package models for customer VPCs and air-gapped environments, and work with the Runtime team on how they're served.
- Turn telemetry into training data: Build the loop from production agent traces and tool-call data to better models, within the data boundaries our enterprise customers require.
- Set the direction: Choose the ML stack, write the strategy, and help decide who we hire next into ML.
- Decide what to build: Use your depth in modern agent systems (harnesses, memory, skills) to pick where models make agents meaningfully better, and to call early whether an approach is going to work.
- Use AI to compound your own output: Projects that take a week today should take a day next time.
Required Skills
- 7+ years of software engineering experience, with 4+ years training and shipping production ML systems. Formal title matters less than the work.
- You've trained or fine-tuned models that went to production and moved a metric customers cared about, not just a leaderboard number.
- Expertise in production agent systems: harnesses, memory, skills, tool use, and sub-agents. You know the nuances well enough to help decide what we build, and have intuition about what will actually work before we build it.
- Know how and when fine-tuning works and how to apply it. What data is needed, and how to derive it from what raw data telemetry gives you.
- Evals you'd defend in a review. You have the statistics fluency to say whether a small delta is real or noise.
- You've deployed models under real constraints like tight latency budgets, limited GPUs, or someone else's infrastructure (vLLM, ONNX, TensorRT, llama.cpp, or equivalent).
- Strong Python for training and ML work, plus TypeScript or Go for the production services that serve your models.
- You pick the stack, write the doc, and can still defend the decision a year later.
- A do-er, not a researcher-in-residence. You'd rather ship a working v0.5 next week than a polished v2.0 next quarter.
- Comfort with ambiguity: early team, a charter that will expand, decisions made with incomplete data.
- An insatiable desire to ship.
Bonus Points
- You've shipped ML into enterprise on-prem or regulated environments (financial services, healthcare, government).
- Tool-use benchmark or eval work: BFCL, τ-bench, ToolBench, MCP evals, or equivalent.
- Familiarity with the MCP (Model Context Protocol) ecosystem. Extra bonus if you've filed an issue against the spec.
- You've built training-data pipelines from production traces with privacy controls in place.
- You've been the first ML hire somewhere before, and you'd do some things differently this time.
- Open-source contributions or published work that survived contact with other engineers.
- 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 is in person at our San Francisco office, and 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.
Compensation: Starting at $230,000 base salary, plus equity and competitive benefits.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
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 $263,811 across 2,416 tracked positions. That's 24% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.