Senior Machine Learning Engineer, Gen AI

Senior Machine Learning Engineer, Gen AI Location US Remote Employment Type Full time Location Type Remote Department Technology Autofill from resume Upload your resume here to autofill key application fields. Upload file Drop your resume here! Parsing your resume. Autofilling key fields... Name Email Resume Upload File or drag and drop here Phone Number Check Yes or No to indicate your agreement to receive text message updates from Weave Communications Inc. regarding your job application. Frequency may vary. Message and data rates may apply. Reply STOP to opt out of future messaging. View our privacy policy here: Privacy Policy Yes - I consent to receiving text messagesNo - I do not consent to receiving text messages Do you have a preferred name? Are you legally eligible to work in the US? YesNo Will you now or in the future require visa sponsorship (e.g., H-1B visa)? YesNo Are you related to anyone who currently works at Weave? If so, please list their name! (Please note this is not for referrals. If you would like to be referred, please reach out to the Weave employee directly) Are you a previous Weave employee? Linked In Profile: Location How did you hear about us? How many years of expeirence do you have in machine learning? 0-2 years 2-4 years 5-7 years 8-10 years 10+ years Have you built or contributed to Gen AI systems used in customer-facing products operating at scale (e.g., real users, external APIs, or live traffic)? YesNo What is the largest number of daily active users (DAU) you've supported with an ML or Gen AI system? DAU = distinct users who interact with the Gen AI-powered system each day, not internal evaluation. < 100 DAU 100 - 1,000 DAU 1,000 - 10,000 DAU 10,000 - 100,000 DAU > 100,000 DAU What is the highest request volume (in requests per minute) your Gen AI system handled in production? Please estimate average or peak usage across production deployments. < 10 RPM 10 - 100 RPM 100 - 1,000 RPM 1,000 - 10,000 RPM > 10,000 RPM Which of the following best describes your experience implementing models for Generative AI (e.g., LLMs, image generation, chatbots, etc.)? I have built and deployed Generative AI models in a production environment I have developed proof-of-concept or internal tools using Generative AI techniques I have fine-tuned existing Generative AI models but haven't deployed them I've only used prebuilt Generative AI APIs (e.g., OpenAI, Gemini) in applications I'm familiar with Generative AI but haven't implemented models myself Which of the following responsibilities have you owned as part of your machine learning work? Defined model requirements in collaboration with product, engineering, and/or ML ops teams Designed, trained, and refined ML models Deployed ML models into production environments Built or maintained the infrastructure to support ML deployment (e.g., pipelines, containers, orchestration) Monitored model performance post-deployment (e.g., accuracy, latency, drift) Set up alerts, dashboards, or logging to track ML system health Participated in on-call rotations or handled incidents when ML models degraded or failed Led root cause analysis and remediation efforts for production model issues Which of the following machine learning use cases have you worked on in a professional setting? Natural Language Processing (NLP) Semantic Search Recommendation Engines Predictive Analytics / Forecasting Large Language Model (LLM) Applications Text Classification / Sentiment Analysis Chatbots / Virtual Assistants Voice/Audio Processing (e.g., transcription, ASR, TTS) Image Classification / Computer Vision Anomaly Detection / Fraud Detection Personalization / Dynamic Content Delivery Other None of these Have you implemented Retrieval-Augmented Generation (RAG) techniques in any of your projects involving LLMs? (Select the option that best describes your experience) Yes - in a production environment Yes - in a proof of concept or learning project I'm familiar with RAG, but haven't implemented it yet No, I'm not familiar with RAG Which of the following best describes your experience with containerization and Kubernetes? I have containerized and deployed ML models using Docker and Kubernetes, including managing clusters and scaling workloads I have deployed containerized ML models using Docker and Kubernetes, but haven't managed clusters directly I have containerized ML models with Docker but haven't used Kubernetes I'm familiar with the concepts but haven't used Docker or Kubernetes in practice I have no experience with Docker or Kubernetes Demographic Questions At Weave we embrace diversity. We know that talent of all backgrounds can thrive and contribute to our company culture, as well as amplify our ability to meet the needs of our diverse consumer base of small businesses. Weave is an equal opportunity employer that is committed to inclusive hiring. The following questions give us deeper insight into who is applying to join our team, but are voluntary (optional) and will not impact the hiring outcome. Which categories describe you? Select all that apply to you: American Indian or Alaska Native Black/of African origin East Asian (For example - Chinese, Korean, Japanese) Hispanic, Latinx or Spanish origin Middle Eastern or North African Native Hawaiian or Other Pacific Islander South Asian (For example, Bangladeshi, Bhutanese, Indian) Southeast Asian (For example, Filipino, Indonesian, Vietnamese) Non-Hispanic White or Caucasian I prefer to self describe I don't wish to answer How do you currently describe your gender identity? Woman, female or feminine Transgender woman, female or feminine Man, male or masculine Transgender man, male, or masculine Non-binary/third gender (For example - bigender, gender non-conforming, androgynous) I don't wish to answer I prefer to self describe Do you consider yourself a member of the Lesbian, Gay or Bisexual (LGB) community? Yes No I don't wish to answer Do you identify as a military veteran or service member? Yes No I don't wish to answer Have you been diagnosed with any disability or impairment? Yes No I don't wish to answer Submit Application