Top Natural Language Processing (NLP) Jobs in San Francisco Bay Area, CA

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Build production-grade AI and NLP systems that verify generated claims against scientific, clinical, regulatory, and reference materials. Responsibilities include hybrid evidence retrieval, claim decomposition, entailment and contradiction detection, confidence scoring, abstention logic, human review workflows, evaluation dataset creation, error analysis, and traceable citation-based decisions. Collaborate with Medical, Legal, Regulatory, and scientific stakeholders to deliver high-stakes, reproducible AI solutions.

Lead post-pretraining work on multimodal medical vision-language models: fine-tuning, reward modeling, RL (including RLHF), chain-of-thought and tool-use training, inference-time strategies, and publishable research to enable clinically grounded radiology report generation.

Open invitation to propose a role at a fast-moving voice AI startup. Candidates should describe how they'd contribute, be self-starters who have built something notable, and thrive on a small team. Apply with a resume and a short note. On-site in San Francisco (remote depending on role); perks include meals, offsites, and equity.

The Senior AI Engineer will develop core AI systems for healthcare, focusing on real-time inference and NLP pipelines, ensuring safety and scalability in medical consultations.

Open call to join an early-stage, YC-backed startup building compliant conversational voice and video AI for life sciences. Applicants should submit a brief intro, the type of work they do best, and why they care about life sciences AI. Company emphasizes ownership, autonomy, rapid iteration, AI-first tooling, competitive compensation, and equity.

Design, build, and deploy production-grade ML models and scalable pipelines (training, inference, monitoring) using NLP/LLM, classification, recommendation, and prediction techniques. Collaborate with product and engineering, optimize latency and costs, and implement retraining and observability for healthcare workflow automation.

Build AI-driven product features and platforms for conversational agents: agent architecture, retrieval, evaluation, voice/telephony, simulation/benchmarking, content management, and agent development lifecycle. Collaborate cross-functionally to design, ship full‑stack production systems and tooling that improve agent quality and customer experience.

Lead and mentor the data science team, define research roadmaps, prototype NLP architectures, and engage with industry audiences.

The Research Engineer will develop large language models, conduct research on model architecture, optimize training infrastructure, and lead research projects.