Develop and deploy generative AI and LLM solutions end-to-end for e-commerce: research models, build ETL/data pipelines, run distributed training and experiments, collaborate with product and engineering to deliver production systems that improve recommendations, search, personalization, and customer interactions.
About SpangleAI
Launched in 2025, Spangle AI is the agentic conversion layer connecting AI-led discovery to real-time conversion. We've partnered with enterprise brands like REVOLVE, Alexander Wang, and Steve Madden, delivering up to 50% conversion lifts and 2x ROAS improvements.
We recently closed a $15M Series A. Spangle won NRF’s VIP (Vendor in Partnership) Award for Best AI-Driven Marketing Solution and was recognized in Business of Fashion’s AI startups to Watch.
Founded by serial entrepreneurs with 30+ years scaling AI and commerce at Amazon, Saks, and Gap, we're building the commerce infrastructure for the agentic era, where ChatGPT Shopping, Google AI Overviews, and Meta are reshaping how consumers discover and buy.
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
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