Founding AI Engineer

Company: ClothME Location: Austin, Texas, USA Employment Type: Full-Time Work Arrangement: On-site / Hybrid Reports To: Founder & CEO/CTO Compensation: Competitive Base Salary + Equity Eligibility Relocation Assistance: May be available

About ClothME

ClothME is building a better way for people to discover and shop for fashion.

We're creating a fit-first fashion marketplace that helps people discover clothing likely to fit them and their families, while helping fashion brands, boutiques, and retailers reach relevant shoppers.

Our platform brings together personalized fit intelligence, product discovery, merchant technology, and creator-driven commerce to make fashion shopping more convenient, personalized, and accessible.

For fashion businesses, ClothME provides an opportunity to acquire new customers, increase online and in-store sales, and reduce fit-related returns through a performance-based model.

There are no setup, subscription, or listing fees. ClothME earns a commission when it generates a sale.

Our long-term vision is to become the operating system for fashion retail.

We're an early-stage technology company with ambitious plans to grow across North America and eventually expand internationally.

We're looking for a Founding AI Engineer who wants to help build the intelligence behind that future.

About the Role

As a Founding AI Engineer, you'll play a central role in developing and improving the artificial intelligence systems powering ClothME.

You'll work directly with our Founder and CEO/CTO and collaborate closely with Backend Engineering, Mobile Engineering, Product, and Design to build intelligent systems that make fashion discovery more personal, accurate, and useful.

This is a hands-on engineering position combining applied machine learning, software engineering, experimentation, and production AI development.

You'll help improve our fit intelligence capabilities, build recommendation and ranking systems, develop personalized shopping experiences, and explore new applications of AI across our marketplace.

You'll also help establish the technical foundation for future AI-powered products and services as ClothME grows.

We're not looking for someone who only wants to experiment with models.

We want someone who enjoys turning research and ideas into reliable products that solve real problems.

As ClothME expands, there will be opportunities to specialize, take ownership of larger technical initiatives, mentor other engineers, and grow into senior technical or leadership positions.

Your Mission

Build and improve the intelligence that helps people discover fashion likely to fit them, supports better shopping decisions, and enables merchants to reach relevant customers.

What You'll Do

Fit Intelligence & Size Recommendations

Help develop and improve the systems that make fit-first shopping possible.

Responsibilities include:

• Design and improve machine learning approaches for fit and size recommendations.
• Work with user-provided measurements, height, photographs, and other permitted fit-related inputs.
• Develop models and algorithms that estimate relevant fit characteristics.
• Build category-specific sizing recommendation capabilities.
• Explore approaches to matching individual Fit Profiles with product sizing information.
• Improve fit recommendation accuracy and reliability.
• Evaluate model performance across different body types, product categories, and brands.
• Develop confidence measures and methods for handling uncertain recommendations.
• Collaborate with Backend and Mobile Engineering to integrate fit intelligence into production experiences.
• Identify opportunities to improve fit-related data quality and model evaluation.

Computer Vision & Image Understanding

Develop computer vision capabilities relevant to fashion and fit intelligence.

Responsibilities include:

• Explore image-based approaches to extracting useful fit-related information.
• Develop and evaluate computer vision models.
• Work with image processing and feature extraction techniques.
• Evaluate pretrained models and determine when fine-tuning is appropriate.
• Improve image processing efficiency and reliability.
• Build secure workflows for handling user-provided images.
• Work with Engineering to optimize image storage, processing, and inference.
• Evaluate model performance under different image conditions.
• Identify limitations and sources of prediction error.
• Help establish responsible practices for image-based AI.

Recommendation Systems & Personalization

Build intelligent systems that help shoppers discover relevant products.

Responsibilities include:

• Develop personalized product recommendation algorithms.
• Design product ranking and relevance models.
• Combine fit compatibility with shopping preferences and behavioral signals.
• Explore content-based, collaborative, and hybrid recommendation approaches.
• Develop strategies for new users and products with limited historical data.
• Improve recommendation diversity and relevance.
• Support personalized product feeds and discovery experiences.
• Build systems that learn from appropriate user interactions and feedback.
• Evaluate recommendation performance through offline testing and controlled experiments.
• Collaborate with Product and Growth on improving shopping engagement and conversion.

Intelligent Search & Product Discovery

Help improve how customers find products on ClothME.

Responsibilities include:

• Develop search relevance and ranking capabilities.
• Explore semantic search and natural language understanding.
• Improve product retrieval using structured attributes and embeddings.
• Combine fit compatibility with search relevance.
• Support filtering and ranking across brands, product categories, and locations.
• Develop approaches to understanding shopper intent.
• Improve search performance and result quality.
• Evaluate search relevance using appropriate metrics and user feedback.
• Collaborate with Backend Engineering on scalable search infrastructure.

Applied Generative AI & LLM Integration

Help develop practical AI-powered experiences for shoppers and merchants.

Responsibilities include:

• Evaluate and integrate large language models where appropriate.
• Build AI-assisted shopping and product discovery capabilities.
• Explore conversational shopping experiences.
• Develop retrieval-augmented generation workflows where useful.
• Implement structured model outputs and reliable tool integrations.
• Evaluate model accuracy, usefulness, latency, and cost.
• Design safeguards against inaccurate or unsupported AI responses.
• Explore AI tools that improve merchant productivity.
• Build reusable AI services and integrations.
• Monitor and improve production AI performance.

Merchant Intelligence & Marketplace Insights

Help develop intelligent tools that support fashion businesses.

Responsibilities include:

• Analyze marketplace and product performance data.
• Explore demand forecasting and product performance prediction.
• Identify opportunities to improve product recommendations and merchandising.
• Develop data-driven insights for merchants.
• Support future inventory and assortment optimization capabilities.
• Collaborate with Merchant Partnerships to understand business needs.
• Evaluate how AI can improve marketplace efficiency and merchant outcomes.
• Help ensure merchant insights are accurate, useful, and privacy-conscious.

Machine Learning Engineering & Infrastructure

Build reliable systems that support the development and deployment of AI.

Responsibilities include:

• Develop reusable machine learning pipelines.
• Prepare and validate training and evaluation datasets.
• Build model training and experimentation workflows.
• Deploy models and inference services into production.
• Improve model serving performance and reliability.
• Monitor model quality, latency, and operational costs.
• Implement appropriate model versioning and rollback processes.
• Collaborate with Backend Engineering on infrastructure and API integrations.
• Improve reproducibility, testing, and documentation.
• Contribute to the evolution of ClothME's AI architecture.

Data Quality, Evaluation & Responsible AI

Help ensure ClothME's AI systems are reliable, secure, and useful.

Responsibilities include:

• Define appropriate model evaluation metrics.
• Build testing datasets and evaluation workflows.
• Identify data quality issues and potential model biases.
• Evaluate performance across different user groups and product categories.
• Monitor model drift and changes in prediction quality.
• Implement appropriate privacy and data protection practices.
• Minimize unnecessary collection and retention of sensitive data.
• Work with Engineering to secure model inputs and outputs.
• Document model limitations and assumptions.
• Help establish responsible AI development practices.

Engineering Collaboration

• Work directly with the Founder and CEO/CTO on AI strategy and priorities.
• Collaborate with Backend and Mobile Engineering to ship AI-powered features.
• Participate in architecture and technical planning discussions.
• Review code and contribute to engineering best practices.
• Share research findings and technical recommendations.
• Help evaluate new models, tools, and technologies.
• Contribute to documentation and knowledge sharing.
• Support future AI engineering recruitment and onboarding as the team grows.

Required Qualifications

We're looking for strong engineering fundamentals, practical machine learning experience, and the ability to deliver production systems.

You should have:

• 4+ years of professional experience in machine learning engineering, applied AI, software engineering with substantial ML responsibilities, or a related field, or equivalent demonstrated expertise.
• Strong proficiency in Python.
• Solid understanding of machine learning fundamentals, model training, evaluation, and deployment.
• Experience building and deploying machine learning systems in production.
• Practical experience with PyTorch, TensorFlow, or comparable frameworks.
• Experience working with structured and unstructured datasets.
• Strong understanding of data preprocessing and feature engineering.
• Experience designing APIs or services that expose ML capabilities.
• Familiarity with cloud-based development and deployment.
• Strong debugging and problem-solving abilities.
• Ability to evaluate model performance using appropriate metrics.
• Experience with Git, testing, and collaborative development.
• Ability to communicate technical concepts clearly.
• Comfortable taking ownership of projects from experimentation through production.
• Interest in consumer technology, personalization, computer vision, or recommendation systems.

A specific degree is not required if you can demonstrate relevant experience and technical capability.

Preferred Qualifications

Experience with one or more of the following would be an advantage:

• Computer vision and image understanding.
• Recommendation systems and ranking models.
• Search relevance and information retrieval.
• E-commerce or consumer marketplaces.
• Fashion technology or sizing recommendation systems.
• Human pose estimation or body measurement estimation.
• Large language models and generative AI.
• Retrieval-augmented generation.
• Vector databases and embeddings.
• Model fine-tuning and optimization.
• MLOps and production model monitoring.
• A/B testing and experimentation.
• Large-scale data processing.
• Low-latency inference systems.
• Privacy-preserving machine learning.
• Early-stage technology companies.

Tools & Technology

You don't need experience with every technology listed below. We value strong fundamentals and the ability to select appropriate tools.

The following technologies are relevant to the types of AI systems ClothME is building or may develop.

Programming & Data

• Python
• SQL
• TypeScript
• NumPy
• Pandas
• Polars

Machine Learning

• PyTorch
• TensorFlow
• Scikit-learn
• XGBoost
• LightGBM

Computer Vision

• OpenCV
• TorchVision
• Hugging Face Transformers
• Image processing and vision model frameworks

LLMs & Generative AI

• OpenAI APIs
• Anthropic APIs
• Open-source language models
• Hugging Face
• Structured generation and tool-calling frameworks

Recommendation & Search

• Embedding models
• Vector similarity search
• pgvector
• Elasticsearch or OpenSearch
• Recommendation and ranking frameworks

MLOps & Experimentation

• MLflow
• Weights & Biases
• Docker
• GitHub Actions
• Model evaluation and monitoring tools

Cloud & Infrastructure

• AWS or comparable cloud platforms
• Containerized inference services
• Object storage
• Scalable model-serving infrastructure

Databases

• PostgreSQL
• Redis
• Vector-enabled data stores

*Our technology environment continues to evolve. The exact production stack will be confirmed during the interview process. We encourage candidates with equivalent experience to apply.*

What Success Looks Like

First 30 Days

• Understand ClothME's existing fit intelligence capabilities and AI-related architecture.
• Become familiar with available data, current models, and product requirements.
• Review existing model evaluation approaches and technical limitations.
• Identify opportunities to improve accuracy, reliability, and performance.
• Set up development and experimentation workflows.
• Begin contributing meaningful improvements.

First 90 Days

• Take ownership of a defined AI capability or improvement initiative.
• Establish or improve evaluation methods for relevant models.
• Deliver measurable improvements to an existing AI workflow or feature.
• Collaborate with Backend and Mobile Engineering on production integration.
• Identify data quality and performance bottlenecks.
• Contribute to AI engineering documentation and technical planning.

First 12 Months

• Help improve the accuracy and reliability of fit recommendations.
• Deliver production AI capabilities supporting personalization or product discovery.
• Establish reliable model evaluation and monitoring practices.
• Improve inference performance and operational efficiency.
• Contribute to a scalable AI architecture.
• Help build the technical foundation for future intelligent shopping experiences.
• Support the development of AI engineering practices as ClothME grows.

How Performance Will Be Measured

We believe successful AI engineering should create measurable improvements in product quality, reliability, and customer outcomes.

Performance may be evaluated through:

Fit Intelligence

• Recommendation accuracy against validated fit outcomes.
• Coverage across supported product categories.
• Reduction in unsupported or low-confidence recommendations.
• Fit-related customer feedback.

Recommendations & Discovery

• Recommendation relevance.
• Search quality.
• Product engagement.
• Conversion and retention improvements attributable to tested changes.

Production Reliability

• Inference latency.
• Model availability.
• Error rates.
• Cost per inference.
• Monitoring and incident resolution.

Engineering Quality

• Testing and documentation.
• Model reproducibility.
• Code quality.
• Successful production deployments.
• Collaboration with engineering and product teams.

Specific targets will be established based on available data, product priorities, and the maturity of each AI capability.

Who Will Thrive Here

You'll likely enjoy this role if you:

• Love solving difficult technical problems.
• Enjoy combining machine learning with software engineering.
• Care about building products that people actually use.
• Prefer measurable results over impressive demonstrations.
• Are comfortable working with imperfect data and evolving requirements.
• Think critically and challenge assumptions constructively.
• Enjoy collaborating with engineers, designers, and product teams.
• Take ownership of your work.
• Value experimentation and continuous improvement.
• Understand that reliable AI requires careful evaluation.
• Want to contribute to a company with ambitious long-term technology goals.

We're looking for someone who is curious, technically capable, pragmatic, and motivated by meaningful challenges.

Compensation & Benefits

ClothME aims to provide competitive compensation and opportunities for professional development.

Depending on eligibility and finalized company policies, benefits may include:

• Competitive base salary.
• Equity participation for eligible employees.
• Health, dental, and vision benefits.
• Paid time off and company holidays.
• Flexible working arrangements.
• Learning and professional development opportunities.
• Modern engineering equipment.
• Access to appropriate development and experimentation resources.
• Relocation assistance where applicable.
• Long-term career development opportunities.

Final compensation and benefits will be communicated during recruitment.

Career Growth & Development

This is a founding engineering position with meaningful opportunities for technical and professional growth.

As ClothME's AI capabilities expand, opportunities may emerge across fit intelligence, computer vision, recommendation systems, search, applied generative AI, and machine learning infrastructure.

Depending on your interests, performance, and company needs, potential career paths include:

• Senior AI Engineer
• Staff Machine Learning Engineer
• Principal AI Engineer
• AI Engineering Lead
• Director of AI Engineering
• VP of AI Engineering

These are potential career paths, not guaranteed promotions.

We believe advancement should reflect demonstrated technical impact, collaboration, leadership ability, and the evolving needs of the company.

Employees who prefer to remain deeply technical should have opportunities to advance without moving into people management.

Interview Process

Our interview process focuses on practical machine learning experience, software engineering ability, technical judgment, and collaboration.

Stage 1 — Initial Conversation

Discuss your background, AI engineering experience, and interest in ClothME.

Stage 2 — Founder & CTO Interview

Meet with the Founder to discuss ClothME's AI vision, technical challenges, and product priorities.

Stage 3 — Applied AI & Machine Learning Discussion

Walk us through machine learning systems you've personally built or deployed, including model selection, evaluation, deployment, and lessons learned.

Stage 4 — Practical Technical Exercise

Complete a focused, job-relevant exercise or discuss an existing project demonstrating your applied AI skills. We aim to respect candidates' time and avoid unnecessarily lengthy assignments.

Stage 5 — Engineering Collaboration & Product Thinking

Discuss your approach to working with engineering teams, handling uncertainty, evaluating trade-offs, and delivering reliable AI products.

Stage 6 — References & Offer

For shortlisted candidates, complete reference checks and discuss compensation, responsibilities, and next steps.

Why Join ClothME?

At ClothME, you'll have an opportunity to help build the intelligence behind a new approach to fashion commerce.

You'll work directly with the Founder and CEO/CTO and collaborate with engineers and product teams to develop AI capabilities that help people shop more confidently and help fashion businesses reach relevant customers.

Your work will contribute to fit intelligence, personalized discovery, intelligent search, and future AI-powered shopping experiences.

You'll have meaningful technical ownership, opportunities to contribute ideas, and room to grow as the company expands.

We're building a team that values curiosity, ownership, technical excellence, collaboration, and measurable results.

If you're excited about applying AI to real-world problems and helping build a technology company with global ambitions, we'd love to hear from you.

Join us in building a better future for fashion commerce.

Equal Opportunity

ClothME is committed to providing equal employment opportunities.

We welcome qualified applicants from diverse backgrounds and evaluate candidates based on their experience, capabilities, and ability to contribute to our mission.

Reasonable accommodations are available throughout the recruitment process.

How to Apply

Apply through the ClothME Careers page:

https://clothme.io/careers

Please include your resume and, if available, links to relevant projects, GitHub repositories, technical publications, or examples of machine learning systems you've built.

We'd especially love to learn about an AI system you personally helped develop, how you evaluated it, and the impact it had.