Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Research and engineer large-scale image and video diffusion models. Train and optimize distributed models on GPU clusters, implement parallelism strategies, profile and debug training infrastructure, improve data and model quality, design experiments and evaluations, and develop fault-tolerant solutions for hardware and numerical issues. The role requires rapid iteration, strong research judgment, custom data pipeline development, and hands-on expertise with PyTorch, distributed training, low-precision computation, and diffusion training across pretraining, preference optimization, and reinforcement learning.

Research and develop posttraining methods for large-scale image and video diffusion models. Responsibilities include fine-tuning, reinforcement learning, preference optimization, distillation, reward modeling with VLMs, LLM prompt expansion, evaluation design, safety alignment, distributed training, and inference optimization. The role partners with data, research, and engineering teams to improve model quality, aesthetics, controllability, and product integration.

Build and improve AI creative products using SvelteKit or React, including real-time collaboration, canvas tooling, durable workflows, public APIs, and observability. The role may also involve Python ML inference on GPU clusters and multi-cloud Kubernetes infrastructure. Engineers own features end-to-end, from ideation through launch and measuring impact, while contributing to robust, revenue-driving products for creative users.

Build full-stack web and mobile APIs and interfaces using SvelteKit, TypeScript, and Node.js. Develop responsive real-time creative tools, optimize frontend performance, manage PostgreSQL and Redis services, and support image and video generation pipelines. Additional work includes public API development, WebSocket and HTTP/3 troubleshooting, observability, enterprise authentication features, and infrastructure scaling with Kubernetes and AWS EKS GPU clusters.

Own post-sale customer success, including onboarding, adoption, renewals, and account expansion. Help creative teams integrate Krea into daily workflows, communicate product updates, track customer usage, identify growth opportunities, and manage renewals. Partner with creative leads on webinars and workshops, relay customer feedback to product and marketing, and contribute to product adoption in an early-stage AI company.

The role involves training and developing large-scale image and video models, optimizing data pipelines, and conducting experiments in a high-performance computing environment.

The role involves designing and managing distributed systems infrastructure for AI workloads, optimizing data pipelines, and collaborating on machine learning projects.

The ML Engineer will architect and build personalization and recommendation systems, design user preference algorithms, collaborate with teams, and improve systems from research to production.

Build and run backend systems spanning the SvelteKit app, Postgres/Redis/ClickHouse data stores, Python ML inference services, and Kubernetes clusters. Own design, monitoring, metrics, and incident response. Implement durable execution for long workflows, improve public APIs, and add enterprise features like ACLs and audit logs.

Lead enterprise onboarding and enablement for Krea: run demos, lead workshops, design production workflows, gather customer feedback, and relay product insights to improve the product.