About SpangleAI
AI is fundamentally changing how people discover products, make decisions, and shop. We’re building the intelligence that helps retailers compete and win in this new world.
Spangle is building an AI-native platform that helps retailers understand customer demand, decide how to respond, and turn those decisions into revenue. Our technology brings together intelligence about each retailer’s products, customers, and business, and puts it to work across AI discovery, paid acquisition, onsite commerce, and emerging agentic shopping experiences.
We’ve partnered with leading brands including REVOLVE, Steve Madden, Alexander Wang, and Express, delivering measurable improvements in conversion, advertising performance, and AI-driven revenue.
Founded by experienced AI and commerce leaders, Spangle has raised $21 million and earned recognition from the National Retail Federation and Business of Fashion.
We’ve built sophisticated technology, won the trust of major retailers, and demonstrated what’s possible.
Now we’re looking for an exceptional leader to help turn that foundation into a repeatable growth engine.
The Role
We are seeking a ML Scientist that will be responsible for building GAI/LLM models end-to-end, from developing the data pipeline to model deployment, to solving real-world problems in the e-commerce sector. You will also collaborate with cross-functional teams to deliver solutions that delight our customers and shoppers.
This role is preferably based in Seattle, the Bay Area, or Austin. Join our founding team to drive product innovation and contribute directly to the growth of our dynamic startup.
What you'll own:
- AI/ML Research and Innovation: Conduct cutting-edge research in Generative AI (GAI) and Large Language Models (LLMs), staying at the forefront of AI/ML advancements. Identify and explore novel algorithms, architectures, and techniques to enhance model performance, scalability, and efficiency in e-commerce applications
- Implementation: Train, deploy, and optimize GAI and LLM algorithms and models to improve product recommendations, search relevance, personalization, and customer interaction
- Data Engineering: Design, build, and manage ETL processes to gather data from various sources, transform it into a usable format, and load it into a data warehouse or data lake
- Delivery: Collaborate with product, engineering, and data teams to identify opportunities for applying generative AI and LLMs to solve complex problems and enhance customer experiences
- Evaluation: Design and conduct experiments to evaluate the performance and effectiveness of generative and language models in an e-commerce context
What We're Looking For
Education
- Ph.D. or Master’s degree in AI, Machine Learning, Data Science, Computer Science, Electrical Engineering, Statistics, or a related field with a focus on artificial intelligence
Experience
- 2-5 years of experience, proven experience in developing and deploying AI applications end to end in real-world applications, preferably in e-commerce or a related field
Technical Skills
- Deep expertise in generative models (e.g., GANs, diffusion models, autoencoders) and Large Language Models (e.g., GPT, BERT, T5, LLaMA)
- Experience with LLM fine-tuning, RL post training, prompt engineering, and deploying LLMs for applications such as natural language understanding, content generation, and recommendation systems
- Strong understanding of the architecture and training techniques for transformer-based models, attention mechanisms, and optimization strategies for LLMs
- Expertise in distributed training of large-scale models, including using parallelization and optimization techniques for handling large datasets
- Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, Hugging Face, and libraries focused on GAI/LLM development
- Familiarity with data warehouse and data pipeline technologies (e.g., Amazon Redshift, Google BigQuery, Snowflake, Apache Airflow)
- Knowledge of cloud platforms and services (e.g., AWS, Google Cloud, Azure) for deploying and scaling machine learning models, especially those involving LLMs and GAI
- Understanding of reinforcement learning and its applications within generative AI and LLMs for decision-making, personalization, or conversational AI systems.
Our Culture
- GenAI-native in how we build, sell, and operate
- High ownership, low overhead, and bias toward action
- Focus on speed, experimentation, and execution
- Deep emphasis on creating clear, demonstrable customer value
- Preference for builders and operators over hierarchy
- Strong belief in in-person collaboration
Why Spangle
- Meaningful ownership and impact at an early stage with ample career growth opportunities
- Competitive salary with uncapped commission and equity
- Benefits: Health, dental, and vision insurance, 401(k), Unlimited PTO
