ML Engineer - NLP

ML Engineer - NLP Job type: Full Time · Department: Engineering (R&D) · Work type: Hybrid Palo Alto, California, United States Our Mission Join Avoma and work on some of the most challenging NLP problems in our mission to make every meeting more productive. Our intelligent meeting assistant helps professionals get insights and automate common tasks required for any business meeting. We leverage the latest NLP techniques to generate notes, analyze data, and automate agentic workflows so our users can spend more time getting things done. We are a venture-funded early-stage startup, have 1000+ paid B2B customers, and are growing consistently month over month. The Opportunity We are looking for a Deep Learning / NLP Engineer to improve and build our systems to extract key insights and topics from conversations. For our insights engine, you’ll need to apply SOTA NLP techniques to take on challenges like identifying different topics of interest and summarizing meeting transcripts to succinct notes. At Avoma you will: Improve Avoma’s Insights Engine Create new models to be used in the production Work with state-of-the-art LLMs to solve complex tasks Develop and improve Deep Learning models using the latest frameworks Build bleeding edge systems to power Ask Avoma and Avoma Agents Stay up to date on the latest state-of-the-art papers in NLP Work with large datasets of over a million documents Requirements At least 2+ Years of experience with Machine Learning & NLP in a production environment Experience developing and debugging in Python Experience working with GPT, Claude, Gemini, or other similar LLMs. Experience writing and shipping scalable, high-performant and clean code Experience training Deep Neural Networks Experience with Tensorflow, PyTorch, or similar frameworks Excellent in both spoken and written communication skills Curious, self-motivated, and loves challenging problems Benefits A transparent salary structure: $120k - $140k per year (DOE) Stock Option Plan Fast-paced, collaborative work environment Paid Time Off (Vacation, Sick and Public Holidays) #J-18808-Ljbffr