Job description

You'll work with semantic retrieval systems, and ranking models to design, optimize, and deploy relevant, personalized, and context-aware experiences. You'll build semantic retrieval, embedding, reranking, and retrieval-augmented generation systems that improve search quality and AI-powered experiences. You'll improve the underlying software infrastructure, optimize solutions for low-latency inference, and adapt large foundation models into smaller, highly capable models that operate efficiently under on-device memory, compute, power, and latency constraints. Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. Background in machine learning, deep learning, natural language processing, information retrieval, search, recommender systems, or generative AI. Experience with semantic retrieval, embedding models, reranking, or retrieval-augmented generation systems. Programming skills in Python and/or C/C++, with experience building production-quality software using modern machine learning frameworks such as PyTorch, JAX, or TensorFlow. Master's or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field. Experience building and deploying semantic search, retrieval, or ranking systems at scale in a production environment. Hands-on experience with retrieval-augmented generation, dense retrieval, or neural reranking pipelines. Experience adapting or distilling large foundation models into compact, efficient models for on-device deployment. Familiarity with model optimization techniques such as quantization, pruning, or knowledge distillation. Experience designing and running offline evaluations and online experiments (A/B testing) to measure search quality, ranking, or model performance. Strong understanding of query understanding, intent modeling, or personalization in search or recommendation systems. Experience working across cross-functional teams to ship AI-powered features in consumer products

Requirements

Must-haves

Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
Background in machine learning, deep learning, natural language processing, information retrieval, search, recommender systems, or generative AI.
Experience with semantic retrieval, embedding models, reranking, or retrieval-augmented generation systems.
Programming skills in Python and/or C/C++, with experience building production-quality software using modern machine learning frameworks such as PyTorch, JAX, or TensorFlow.
Master's or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
Experience building and deploying semantic search, retrieval, or ranking systems at scale in a production environment.
Hands-on experience with retrieval-augmented generation, dense retrieval, or neural reranking pipelines.
Experience adapting or distilling large foundation models into compact, efficient models for on-device deployment.
Familiarity with model optimization techniques such as quantization, pruning, or knowledge distillation.
Experience designing and running offline evaluations and online experiments (A/B testing) to measure search quality, ranking, or model performance.
Strong understanding of query understanding, intent modeling, or personalization in search or recommendation systems.
Experience working across cross-functional teams to ship AI-powered features in consumer products

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningpython

Platform: apple
Effort: medium