Senior Machine Learning Engineer, Proactive

Description

You'll design, train, fine-tune, and optimize transformer-based language models for on-device deployment, and build semantic retrieval, embedding, reranking, and retrieval-augmented generation systems that improve search quality and AI-powered experiences. You'll develop models for query understanding, intent prediction, personalization, and ranking, while researching new approaches to model compression, quantization, and low-latency inference. You'll partner with engineers, researchers, product managers, and designers to bring new AI capabilities from research into production driving technical strategy and leading projects from early exploration through large-scale deployment. This is an opportunity to explore new applications of foundation models, multimodal AI, and agentic retrieval, shaping the next generation of proactive, intelligent user experiences.

Minimum Qualifications

Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.

5+ years of industry or research experience developing machine learning systems.

Background in machine learning, deep learning, natural language processing, information retrieval, search, recommender systems, or generative AI.

Experience training, fine-tuning, or deploying transformer-based models and large language models.

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.

Preferred Qualifications

Master's or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.

Experience optimizing machine learning models for resource-constrained environments, including model compression, quantization, pruning, and knowledge distillation.

Experience with on-device machine learning or mobile inference frameworks.

Experience building retrieval-augmented generation, vector search, embedding retrieval, or semantic search systems.

Experience working with transformer architectures such as BERT, T5, Llama, Gemma, Mistral, or other foundation models.

Experience evaluating language models, designing AI quality metrics, and building offline evaluation pipelines.

Experience building large-scale production search, recommendation, or personalization systems.

Ability to prototype ideas, solve ambiguous problems, and deliver production-quality machine learning solutions.