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About This Role
About Decagon
Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences.
Our technology enables industry\-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel.
We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world\-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others.
We’re an in\-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team.
About the Team
Founder’s Office is where the hardest problems land, and we help solve them for the entire business. We are a small team of former founders, operators, and investors embedded directly with leadership, taking on work of any kind, no matter how different or unique, to improve business operations. Strategy, corporate development, pricing, special projects, and analytics are a few examples. We are analytical and critical cross\-functional thinkers from a diverse set of multidisciplinary backgrounds. If it is ambiguous, urgent, or high\-stakes, we are probably already thinking about it.
About the Role
As an AI Transformation Manager, you will help accelerate Decagon’s goal of applying AI to becoming the most productive business in the world. You will drive AI adoption across the business and help build an AI\-first mindset, so every team leverages new capabilities and has the tools to act quickly and effectively.
Working closely with senior leadership and XFN teams, you’ll parachute into orgs with problems, co\-build cutting\-edge AI software for non\-engineers (and eventually EPD), and use it to drive massive change. You are wearing many different hats \-from being a vibe coder, part BizOps maven, strategist, data analyst, sometimes product manager, even chief of staff. You will integrate best\-in\-class tools and build AI agents to drive efficiencies across the entire company.
This is a very high agency, high impact role. As such, we believe in building together, quickly. This is a fully in\-office role based in our San Francisco office 5 days a week.
In this role, you will
- Map out the Stack: lead the technical integration and optimization of external tools (e.g., Salesforce, Clay, Gong, enrichment APIs) into a cohesive, automated engine. Then, map processes, build workflows using tools like Dust, Gumloop, n8n, Notion, Cursor, or Claude, plugins, and orchestrate integrations.
- Build AI Agents: design and deploy agents that optimize account research, reduce manual data entry, and enhance outbound personalization using various tools \& APIs, and ship to prod dozens of AI\-driven automations or copilots across the company within the first 90 days.
- Programmatic enablement: partner with leadership to translate company\-wide strategies into automated workflows, custom internal tools, and programmatic systems. Unlock hundreds of Decagon team members with their “aha” moment of your product \- and therefore drive high usage.
- AI Culture Driver: build an AI\-native culture for the entire business, by identifying and enabling internal “AI champions,” and builders. Programmatically define templates and best practices into team norms. Turn them into people who can’t live without the automation and will be *very unhappy* if it is taken away.
- Analytics: measure success by going deep into data and analytics, and have an iterative mindset to problems. Become customer obsessed: customers being our internal stakeholders. We will measure by hours the company saved, and additional return on output efficiency based on how much additional work they’re able to deliver.
Your background looks something like this
- Experience: ideally, 1\-3 years in a high growth technical environment. \*\*\*\*Roles that are inherently self\-starters, entrepreneurial, or technical are also great fits, looking to make the switch into this new and emerging critical function for all companies. We optimize for high growth and slope more than anything.
- AI prompting foundations: some experience in some form with AI enablement, AI operations, or AI engineering roles. You are generally comfortable prompting your way to solutions, and sometimes needing to get into code / working with APIs, etc.
- Builder DNA: you are a curious learner and builder, skilled with new AI tools like Clay, Claude Code, Cursor, Notion, Unify, Dust/Gumloop/n8n/Zapier, and scraping / leveraging search APIs like Exa, Parallel, Browserbase, and more. Some SQL, BI, and scripting experience is a plus. You are a nerd at heart when it comes to AI enablement tooling
- Get it done posture and owner: a bias toward action and building "v1 MVP" today. You thrive in the ambiguity of a fast\-paced AI startup. An end\-to\-end owner, not a renter; driven by outcomes. You have the hustle, and figure it out mindset.
- Deep empathy: able to collaborate closely with business teams, understand challenges at a deep level, and deliver rapid solutions. Naturally empathetic, personable, and a strong culture carrier.
- Strong communications: you can produce clear, concise presentation of information, and influence teams to use your products. You run training sessions that drive impact through leadership and communications, in every gap you find.
- Ability to manage cross\-functional teams: able to work hands\-on in workflows across Sales, Post\-Sales, CX, Finance, People/HR, Ops, BizOps, Marketing, EPD, and more. It’s OK if you haven’t done it before.
- Ideally have some program management experience: but not necessary. Experience driving internal enablement and change\-management initiatives.
If you don’t meet every requirement, please still apply. Some of our best candidates thrive once provided the opportunity.
Benefits:
- Medical, dental, and vision benefits
- Take what you need vacation policy
- Daily lunches, dinners and snacks in the office to keep you at your best
Compensation:
$185,000 \- $235,000 \+ Equity
Compensation Range: $185K \- $235K
Salary Context
This $185K-$235K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Decagon, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($210K) sits 26% above the category median. Disclosed range: $185K to $235K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Decagon AI Hiring
Decagon has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, San Francisco, CA, US. Compensation range: $235K - $330K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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