{"id":1311901,"url":"https://alion.io/job/spangle-aiml-scientist","title":"AI/ML Scientist","company":{"id":3780520,"name":"Spangle","domain":"spangle.ai","url":"https://alion.io/company/spangle","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Rippling","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Austin, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":100000,"max_usd":225000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":186},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"BERT","optional":false},{"name":"BigQuery","optional":false},{"name":"ChatGPT","optional":false},{"name":"Diffusion Models","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Hugging Face","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Post-training","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Snowflake","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-02-20T08:22:07Z","employer_posted_date":"2026-02-20","last_verified_at":"2026-09-26T16:55:14Z","board_verified":true,"closed_at":null,"days_open":218,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":218},"description":"About SpangleAI\nLaunched 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.\nWe 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.\nFounded 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.\n The Role\nWe 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.\nThis 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.\nWhat you'll own:\nAI/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\nImplementation: Train, deploy, and optimize GAI and LLM algorithms and models to improve product recommendations, search relevance, personalization, and customer interaction\nData 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\nDelivery: Collaborate with product, engineering, and data teams to identify opportunities for applying generative AI and LLMs to solve complex problems and enhance customer experiences\nEvaluation: Design and conduct experiments to evaluate the performance and effectiveness of generative and language models in an e-commerce context\nWhat We're Looking For\nEducation\nPh.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\nExperience\n2-5 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\nTechnical Skills\nDeep expertise ingenerative models (e.g., GANs, diffusion models, autoencoders) andLarge Language Models (e.g., GPT, BERT, T5, LLaMA)\nExperience with LLM fine-tuning, RL post training, prompt engineering, and deploying LLMs for applications such as natural language understanding, content generation, and recommendation systems\nStrong understanding of the architecture and training techniques for transformer-based models, attention mechanisms, and optimization strategies for LLMs\nExpertise in distributed training of large-scale models, including using parallelization and optimization techniques for handling large datasets\nProficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, Hugging Face, and libraries focused on GAI/LLM development\nFamiliarity with data warehouse and data pipeline technologies (e.g., Amazon Redshift, Google BigQuery, Snowflake, Apache Airflow)\nKnowledge of cloud platforms and services (e.g., AWS, Google Cloud, Azure) for deploying and scaling machine learning models, especially those involving LLMs and GAI\nUnderstanding of reinforcement learning and its applications within generative AI and LLMs for decision-making, personalization, or conversational AI systems.\nOur Culture\nGenAI-native in how we build, sell, and operate\nHigh ownership, low overhead, and bias toward action\nFocus on speed, experimentation, and execution\nDeep emphasis on creating clear, demonstrable customer value\nPreference for builders and operators over hierarchy\nStrong belief in in-person collaboration\nWhy Spangle\nMeaningful ownership and impact at an early stage with ample career growth opportunities\nCompetitive salary with uncapped commission and equity\nBenefits: Health, dental, and vision insurance, 401(k), Unlimited PTO","description_format":"text","description_chars":4441,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity","Growth opportunities","Unlimited PTO","Vision insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Commerce"],"lifecycle":[{"event":"open","at":"2026-09-26T16:55:14Z"}],"liveness":{"score":3,"band":"cold","label":"Long shot","p_open":1,"p_active":0.09,"p_room":0.28,"age_days":218,"expected_fill_days":15,"reasons":["conf:10","win:tail","crowd:junior"],"computed_at":"2026-09-27T02:58:47Z"},"pay":null,"html_url":"https://alion.io/job/spangle-aiml-scientist","json_url":"https://alion.io/job/spangle-aiml-scientist.json","meta":{"generated_at":"2026-09-27T02:58:47Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2731,"day_limit":5000,"remaining_today":2269,"minute_limit":60,"resets_at":"2026-09-28T00:00:00Z"}}}