retrieval-systems jobs in San Diego

Design, develop, and optimize production machine learning systems including model development, inference optimization, and scalable ML infrastructure. Build end-to-end ML pipelines from training through deployment, optimize model inference for latency and cost, integrate LLM/ML models into APIs and microservices, and engineer data pipelines for preprocessing and validation.…See more Requires strong software engineering fundamentals with deep ML expertise, proficiency in Python and systems languages, and experience with ML frameworks, model serving, and distributed computing.

Senior Staff Inbound Product Manager responsible for owning the roadmap, vision, and requirements for conversational AI features within ServiceNow's AI platform. The role requires translating customer feedback into product improvements, guiding product strategy across multiple teams, and steering the development of AI features including disambiguation, clarification, document/multimodal experiences, slot filling, feedback intelligence, and fallback handling.…See more Success requires 12+ years of enterprise product management experience, deep knowledge of conversational AI, generative AI, and agentic systems, plus hands-on experience with AI evaluation, prompt engineering, retrieval systems, and knowledge bases. The person will create PRDs, define testing and quality requirements, support strategic customers through pilots and go-lives, and coordinate dependencies across engineering and design teams.

SHEIN is seeking a Senior Data Engineer to build and productionize GenAI/LLM solutions for data engineering workflows, including code assistance, metadata discovery, and knowledge access. The role focuses on developing RAG and agentic workflows, automating incident triage and log analysis, and integrating AI capabilities into internal developer tools and services.…See more You'll own scoped projects from problem definition through production support, requiring 3+ years of hands-on production experience with strong Python/SQL fundamentals, proven GenAI/LLM application development, and practical knowledge of databases, APIs, data pipelines, and distributed systems.

Design, develop, and optimize machine learning systems and models for production AI platforms, with focus on inference optimization, scalable ML infrastructure, and deployment. Responsibilities include building ML pipelines, optimizing model inference across hardware environments, integrating LLMs and models into APIs and microservices, designing data pipelines for ingestion and feature engineering, and collaborating cross-functionally on end-to-end ML solutions.…See more Requires strong software engineering fundamentals combined with deep ML expertise, proficiency in Python and at least one systems language (C++, Rust, or Go), and solid understanding of ML frameworks, transformer architectures, and model deployment systems.

Design, develop, and optimize machine learning systems for production AI platforms, including model development, inference optimization, and scalable ML infrastructure. Build training-to-deployment pipelines, optimize model serving for latency and cost, and integrate LLMs and generative AI models into microservices and APIs.…See more Develop data pipelines for ingestion, preprocessing, and feature engineering while collaborating cross-functionally with product, platform, and hardware teams to deliver end-to-end ML solutions.

As Principal Software Engineer for AI & Data Platform, you will architect the data foundation for scientific and engineering R&D platforms, designing scalable data processing patterns, ML training pipelines, and intelligent workflow interfaces. You will own end-to-end responsibilities including data modeling for multi-use analytics and ML, building production training and fine-tuning pipelines, model evaluation and benchmarking, and setting engineering standards for the team.…See more The role requires 10+ years shipping production software, expert-level Python, deep experience with large-scale data systems (object storage, analytical processing, training formats), hands-on ML pipeline development, and the ability to set technical direction in early-stage environments while implementing it yourself.

Build the foundational agentic AI layer for a materials-science platform, including multi-model provider abstraction, agent orchestration with stateful checkpoints, retrieval systems, prompt versioning, and comprehensive tracing and evaluation frameworks. You'll design agents that plan and reason over tool calls in production, implement human-in-the-loop safety gates, and ensure all LLM behavior remains auditable and cost-tracked across customers' secure environments.…See more The role demands deep production experience with agentic and LLM systems: async Python, structured outputs, memory and context management, multi-step workflow orchestration, and evaluation harnesses that catch regressions before deployment.