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About This Role
Stuut is transforming accounts receivable for B2B companies—making collections smarter and faster for companies that have historically relied on manual processes that are labor intensive and costly. Our platform is gaining traction with finance teams across industrials, chemicals, and manufacturing sectors from Fortune 10 brands to scaling midmarkets. We're backed by top\-tier investors including a16z, Khosla, Activant, 1984 Ventures and Page One.
The Role
============
We’re hiring a Member of Technical Staff — Internal AI Tooling to build the systems that power how Stuut operates and scales. This role focuses on designing and implementing the internal infrastructure, automation, and AI\-driven workflows that increase leverage across the company—starting with marketing and expanding across sales, operations, and product.
This is a high\-impact builder role for someone who enjoys identifying manual or fragmented processes and replacing them with systems that scale. You will work closely with leadership and cross\-functional teams to design AI agents, automation pipelines, and internal tools that streamline operations and unlock new capabilities.
We're becoming an agent\-first company — not just in our product, but in how we operate. This role is at the center of that shift.
What You'll Do
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### Build Internal AI and Automation Infrastructure
- Design and build AI\-powered agents and automation workflows that eliminate repetitive internal tasks across teams.
- Develop internal tools and systems that improve operational efficiency and reduce manual processes.
- Architect automation pipelines using LLM APIs and orchestration tools that integrate with our core stack, including HubSpot, Slack, Fathom, and Linear.
- Implement evaluation frameworks, logging, and feedback loops to continuously improve AI\-driven workflows.
### Build Marketing and Demand Generation Systems
- Build and maintain data pipelines that support go\-to\-market systems such as CRM enrichment, ICP scoring, contact discovery, and automated lead routing.
- Develop automated outbound and demand generation systems, including AI\-personalized outreach, signal\-triggered workflows, and visitor identification infrastructure.
- Create programmatic account\-based marketing workflows including audience segmentation, personalization systems, and ad platform integrations.
- Partner with marketing leadership to prototype and scale campaign experiments with clear measurement and iteration.
### Build Systems That Scale Across the Company
- Identify operational bottlenecks across teams and replace manual workflows with automated systems.
- Develop internal tools that improve data access, reporting, and operational decision\-making.
- Treat internal tooling like a product—defining requirements, measuring adoption, and iterating based on usage and impact.
- Partner with Engineering, Marketing, and Operations to ensure systems integrate cleanly into company workflows.
You Might Be a Fit If You…
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- Have 5\+ years of full\-stack engineering experience building and shipping scalable systems or products.
- Are highly skilled in Python and TypeScript, with experience building backend services and modern web applications.
- Have experience designing data pipelines, APIs, and system architectures that support complex workflows.
- Are proficient with modern frameworks such as React, Vue, or Angular on the frontend and Django, FastAPI, Flask, or Node.js on the backend.
- Have experience working with cloud platforms (AWS, GCP, or Azure) and modern deployment infrastructure including containerization and CI/CD.
- Enjoy working in early\-stage startup environments where problems are ambiguous and systems are built from scratch.
- Are comfortable collaborating across teams and translating operational needs into technical solutions.
- Take ownership of outcomes and measure your work through real operational impact.
Bonus Points
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- Experience building AI agents or automation systems using LLM APIs or agent frameworks such as LangChain, LangGraph, or CrewAI.
- Familiarity with tools in Stuut’s ecosystem such as HubSpot, n8n, Fathom, Linear, Clay, or the Slack API.
- Experience building internal infrastructure or automation systems at a high\-growth B2B SaaS or fintech company.
Compensation
- Top\-of\-market salary and equity package
- Benefits (for U.S.\-based full\-time employees)
- Medical, dental \& vision insurance coverage for you
- 401(k) \& Match
- Equity
- Flexible PTO
- Parental Leave
Compensation Range: $165K \- $210K
Salary Context
This $165K-$210K 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 stuut, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($187K) sits 12% above the category median. Disclosed range: $165K to $210K.
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.
stuut AI Hiring
stuut has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $210K - $210K.
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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