Build & Refine Classifiers: Develop and maintain multi-class text classification models to categorize creator content, brand mentions, and sentiment with high precision and recall.
Analyze Content Semantics: Utilize Natural Language Processing (NLP) techniques (topic modeling, sentiment analysis, entity extraction) to structure unstructured data from social platforms like TikTok, Instagram, and YouTube.
Bridge ML and GenAI: Experiment with Large Language Models (LLMs) to augment training data, perform few-shot classification, or summarize complex creator data, interfacing with the engineering team to bring these concepts to life.
Own the Data Lifecycle: Write efficient code to clean, preprocess, and tokenize large text datasets, ensuring high-quality inputs for your models.
Measure & Optimize: Work with MLOps to constantly evaluate model performance using standard metrics (F1 score, AUC-ROC) and work to reduce latency for real-time inference needs.
Build & Integrate: Collaborate with engineering to integrate measurement loops into our broader infrastructure (AWS/GCP), ensuring our model lifecycle is automated and observable.

Requirements

NLP Practitioner: You have 2-4 years of experience building and deploying NLP models. You are comfortable with concepts like tokenization, word embeddings, and topic modeling.
Machine Learning Foundation: You have a strong grasp of classical ML algorithms (Random Forest, XGBoost, SVM) and know when to use a simple logistic regression versus a deep learning approach.
Python Proficiency: You are fluent in Python and its data stack (Pandas, NumPy, Scikit-learn). Experience with libraries like Hugging Face or Spacy is a major plus.
Curious about LLMs: While you are grounded in traditional ML, you have a working knowledge of LLM APIs (OpenAI, Anthropic) and prompt engineering, and you are eager to learn how to integrate them into production workflows.
Data Driven: You are comfortable writing complex SQL queries to pull your own data and verify your hypotheses.
Team Player: You can explain complex technical concepts to non-technical stakeholders and collaborate effectively with MLOps engineers to get your models into production.

Benefits

People: work with talented, collaborative, and friendly people who love what they do.
Guidance: utilize our learning platform to fully get the training and tools you’ll need to become successful here from your first day with us.
Surprise meal stipends: work from home can’t stop the enjoyment of someone else making a meal for you!
Work/life harmony: 15 days vacation, floating and set holidays, wellness allowance, and paid parental leave.
Whole Health Package: medical, dental, vision, life, disability insurance, and more.
Savings: a 401k (USA) plan to help you plan ahead.
Work from home stipend: to assist you in setting up a home office that works for you (or buy a new dog leash - your choice!).

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Natural Language Processingtext classificationtokenizationword embeddingstopic modelingclassical ML algorithmsRandom ForestXGBoostSVMPython

Soft Skills

team playercommunicationcollaborationcuriositydata driven