AI Engineer, Applied ML
Designs, builds, and iterates on AI/ML models for personalization, query understanding, and content discovery. Requires 5+ years in ML, deep learning expertise (PyTorch/TensorFlow/JAX), Python, and full ML lifecycle ownership.
About the job
Key Responsibilities
- Apply state-of-the-art ML and LLM techniques to solve problems spanning:
- Personalization (LLM memory, context summarization, retrieval and ranking)
- Query Understanding (intent modeling, rewriting, agentic decomposition)
- Content Discovery (feed ranking and surfacing)
- Rigorously evaluate LLM/ML models with both offline and online techniques, designing experiments and metrics that provide deep insight into quality and impact
- Own the entire model lifecycle from research to production: data analysis, modeling, evaluation, offline/online A/B testing, and iterative improvement
- Collaborate cross-functionally with engineers, PMs, data scientists, and designers to ensure our AI drives meaningful product improvements
- Stay at the forefront of ML/AI innovation by evaluating and incorporating emerging research and algorithms into the product lifecycle
Preferred Qualifications
- 5+ years experience building and shipping robust ML/AI models for large-scale, user-facing or data-driven products
- Deep expertise in deep learning (PyTorch, TensorFlow, JAX), LLMs, information retrieval, content summarization, recommendation systems, NLP, and/or ranking
- Strong software engineering skills (Python, production-quality codebases, collaborative development)
- In-depth experience with the full ML lifecycle: data analysis, feature engineering, iterative model development, rigorous evaluation, and ongoing monitoring/improvement
- Proven collaborator and communicator; excels in high-velocity, cross-functional teams
- Curious, driven by end-user/product impact, and passionate about advancing the state of applied ML and AI
- BS, MS, or PhD in Computer Science, Engineering, or related field (or equivalent experience)
Bonus Points For
- Experience with LLM prompt engineering, Retrieval-augmented generation (RAG) based systems
- Experience in large scale user-centric and content-centric personalization challenges (user modeling, retrieval, content ranking, etc)
- Open-source or published contributions in ML, NLP, IR, or relevant research fields
Skills
PyTorch, TensorFlow, JAX, Python, LLMs, NLP, Information Retrieval, Recommendation Systems, RAG
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