Interested in this AI/ML Engineer role at COMFY?
Apply Now →About This Role
Help creative teams turn Comfy workflows into everyday production.
Comfy is the open, modular platform for visual AI. Artists, studios, developers, and enterprise teams use ComfyUI to build workflows across image, video, audio, and 3D—with control over the models, parameters, and processes behind their work.
We’re hiring our first Customer Success Manager to help our most strategic customers adopt Comfy, get into production, and grow with us.
You’ll work directly with customers while building the foundation of our customer success function. That means leading onboarding, teaching teams how to use Comfy effectively, resolving adoption barriers, and defining how we track customer health, manage renewals, and identify expansion opportunities.
This is a hands-on role for someone who knows ComfyUI, understands how creative and technical teams work, and has built customer relationships that translate into lasting adoption.
What you’ll own
Customer onboarding and adoption
- Lead customers from initial goals to working production workflows, with clear milestones and measurable outcomes.
- Run practical onboarding sessions, workshops, and product deep dives for artists, technical leads, and enterprise teams.
- Help customers navigate workflows, models, custom nodes, and deployment options across ComfyUI, Comfy Cloud, and Comfy API.
- Identify where adoption stalls, troubleshoot the underlying issues, and partner with engineering or our Forward Deployed Creatives when deeper implementation work is needed.
Customer relationships and growth
- Own the ongoing relationship with strategic accounts, from onboarding through renewal and expansion.
- Build success plans around each customer’s production goals, stakeholders, and definition of value.
- Recognize risk early and take action on low usage, unresolved technical issues, or changing customer priorities.
- Identify opportunities to expand adoption across teams, workflows, and use cases.
The customer success function
- Define which customers need dedicated attention and where education, documentation, and self-service can scale.
- Build practical playbooks for onboarding, account management, escalations, renewals, and expansion.
- Establish customer health signals and success metrics, including time to first value, adoption, retention, and account growth.
- Turn recurring customer needs into clear product feedback and work with product and engineering to prioritize improvements.
- Help shape and grow the CS team as the business develops.
What you bring
- Hands-on ComfyUI experience. You’ve built workflows, encountered problems, and worked through them. You can explain your choices and help someone else learn.
- Experience owning customer relationships at a technical SaaS, developer tools, creative software, or AI company—typically five or more years.
- A track record of driving onboarding, adoption, retention, and expansion, with concrete examples of your contribution.
- Experience building processes in an early-stage or rapidly growing business.
- The ability to earn trust with both creative and technical stakeholders, from a production artist or VFX supervisor to a pipeline lead or CTO.
- Comfort discussing models, custom nodes, APIs, and workflow dependencies. Coding experience is helpful; practical technical judgment is essential.
- Clear communication, sound prioritization, and the ability to make progress when requirements are still taking shape.
Experience in film, VFX, gaming, advertising, design, or other creative production environments is especially relevant. So is experience helping an open-source community adopt a commercial product.
What success looks like
Customers reach useful production outcomes faster. Their teams can operate and adapt workflows confidently. Adoption risks surface early, and renewals and expansion follow from demonstrated value.
Inside Comfy, you’ll establish a repeatable customer success approach that helps us serve more customers while keeping a close understanding of their needs.
About Comfy
Comfy gives visual professionals the building blocks to connect models, processing steps, and outputs on an inspectable canvas. Workflows can be adapted, reproduced, and shared—from an artist’s local setup to a team’s production pipeline.
We’re a small team based in San Francisco, working closely across product, engineering, and go-to-market. We value ownership, technical curiosity, direct communication, and people who care about what customers are building.
Explore Comfy, our GitHub, and our blog.
How to apply
Share your resume and a ComfyUI workflow or project you’ve built. Tell us what problem it solves, what you learned, and what you would improve.
Include a brief example of a customer success process you built or significantly improved, and the outcome it produced.
Compensation Range: $180K - $225K
Salary Context
This $180K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At COMFY, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $180K to $225K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
COMFY AI Hiring
COMFY has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $225K - $225K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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