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

Provides strategic legal counsel on product, intellectual property, and AI matters. Oversees the worldwide IP portfolio, develops IP strategy, manages invention disclosures and patent prosecution, advises business and product teams on global IP law and compliance, supports commercial agreement negotiations, and advances product legal strategy. Requires substantial in-house and law-firm experience, a California law license, and expertise in AI, machine learning, and generative AI technologies.

Manage and grow a large global contributor community for Scale’s Generative AI business. Develop engagement, retention, communications, feedback, and dispute-resolution strategies; oversee community platforms, virtual events, sentiment monitoring, and contributor messaging. Use metrics and feedback to improve operations and product strategy, coordinate cross-functional stakeholders, remove blockers, and maintain cohesive communications. The role also includes managing at least one direct report and executing operational initiatives directly.

Software Engineering Interns will build and ship production software for AI systems, including data pipelines, evaluation infrastructure, agentic applications, fraud detection, matching algorithms, contributor tools, and AI infrastructure products. Interns own roadmap work with mentorship and collaborate on scalable, high-quality products. The role requires product engineering experience, prior software engineering internship experience, large-scale data systems experience, and familiarity with Python, TypeScript, React, and/or MongoDB.

Build and ship customer-facing software products and AI applications, including data pipelines, evaluation infrastructure, agentic systems, fraud detection, matching algorithms, contributor tools, and RAG applications. Collaborate directly with customers, process large-scale data, integrate APIs and services, and iterate quickly on high-quality products.

Design and develop shared platform systems, foundational data platforms, cloud infrastructure, orchestration, and software delivery tooling. Partner with stakeholders and cross-functional teams to define and deliver features, improve engineering practices, and communicate technical concepts. The role requires expertise in backend systems, distributed systems, public cloud, containerization, databases, orchestration, and CI/CD, with opportunities to support AI-focused products.

Own the network architecture and standards for a multi-cloud generative AI platform deployed across AWS, Azure, GCP, and customer environments. Design VPCs, routing, private connectivity, DNS, ingress, egress, edge configurations, Kubernetes networking, service mesh, and network policies. Establish repeatable customer-boundary connectivity and access patterns, review proposed solutions, partner on security and compliance, debug hybrid-cloud incidents, and document operational standards. The role serves as the central networking authority across regulated and air-gapped deployments.

Own the technical foundation for building, packaging, executing, verifying, and scaling reinforcement learning environments. Design sandboxed execution, rollout orchestration, trajectory capture, verifier frameworks, environment versioning, and authoring tools. Build high-throughput infrastructure and reliable reward signals, instrument real applications, develop task suites, and defend against reward hacking. Provide staff-level technical leadership across engineering and research teams while shipping complex production systems.

Product Manager for Gen AI Platform to drive strategy, execute product roadmaps, and manage cross-functional teams while ensuring quality training data systems for AI models.

Research and develop novel post-training methods such as SFT, RLHF, reward modeling, and preference optimization for text and multimodal LLMs. Analyze model behavior, improve alignment and generalization, mitigate bias, enhance robustness, and publish findings at leading AI conferences. Collaborate with engineers, researchers, and foundation model labs to establish data-driven AI development practices and provide technical and strategic guidance.

Conduct research on evaluating frontier large language models and AI agents. Analyze model behavior, diagnose capability, reasoning, robustness, and alignment failures, and develop benchmarks for text and multimodal systems. Apply post-training techniques such as supervised fine-tuning, RLHF, and reward modeling to connect failures with training interventions. Collaborate with AI labs, define evaluation best practices, translate findings into technical strategy, and publish research at major conferences.