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

Manage and mature Baseten’s security governance, risk, compliance, privacy, audit, certification, vendor assessment, and customer assurance programs. Translate regulatory and customer requirements into technical controls and product requirements, partnering with Engineering, Product, Security, Legal, and Operations. Coordinate SOC 2, ISO, HIPAA, FedRAMP, and related audits; manage risk assessments, evidence collection, remediation, security questionnaires, training, and continuous improvement in a fast-growing AI platform company.

Design and run post-training pipelines for large language models using supervised fine-tuning, reinforcement learning, reward engineering, synthetic data, and evaluation. Build customer-specific agent environments and translate production data into training signals. Analyze experiments, diagnose reward hacking and optimization instability, support deployment of models to production, publish research, and contribute to open-source training libraries.

Build and maintain revenue data models, dashboards, and documentation connecting contracts, pricing, product usage, billing, and invoices. Reconcile metered usage with billed revenue, govern revenue metrics, support forecasting and monthly close, and investigate data discrepancies. Partner with Finance, Revenue Operations, Finance Systems, Product, and Engineering to ensure reliable reporting for bookings, commitments, ARR, NDR, expansion, and consumption.

The Product Data Scientist will establish data-driven product decision-making at Baseten. Responsibilities include defining product metrics, designing measurement plans, analyzing experiments and customer behavior, forecasting, evaluating platform reliability and releases, and building trusted reporting tools. The role partners closely with Product, Engineering, founders, and GTM to guide roadmap and prioritization decisions across a technical, usage-based AI platform.

Leads and grows an inference performance engineering team focused on optimizing GPU-based AI workloads. Responsibilities include technical roadmap ownership, GPU profiling, inference runtime optimization, model and hardware bring-up, productionizing techniques such as quantization and speculative decoding, and measuring latency, utilization, and cost improvements. The manager partners across engineering and customer-facing teams, maintains high engineering standards, and remains hands-on in solving complex performance challenges.

Develop and productionize high-performance LLM inference techniques across runtimes, scheduling, serving, routing, and GPU kernels. Profile and optimize latency, throughput, memory usage, and cost; support new models and hardware; build benchmarking frameworks; and contribute to open-source inference engines such as vLLM, SGLang, and TensorRT-LLM. The role requires strong programming, GPU architecture, ML library, and LLM optimization knowledge.

Build and operate the distributed inference platform powering large-scale LLM deployments. Responsibilities include orchestration, routing, autoscaling, scheduling, Model APIs, authentication, quotas, metering, observability, benchmarking, and production reliability across Kubernetes, networking, distributed runtimes, and GPU workloads. The role owns projects end to end and partners with performance engineers to deliver efficient, scalable AI inference capabilities and strong developer experiences.

Own full-cycle recruiting for the marketing organization, including role kickoffs, sourcing, candidate experience, process improvement, and offer acceptance. Partner with marketing leaders and hiring managers to hire across product marketing, demand generation, growth, content, brand, and events. Build targeted candidate pipelines, manage multiple searches, and develop expertise in AI infrastructure and technical products while maintaining a high hiring bar.

Own the financial economics of Training and new products, including cost-to-serve modeling, pricing, capacity planning, forecasting, annual plans, and P&L analysis. Partner with Product, Engineering, Infrastructure, and GTM leaders on business cases, launch pricing, margins, and scale decisions. Lead monthly business reviews and define success metrics for new product launches in a fast-growing AI infrastructure company.

Own recruiting operations programs from problem identification through adoption. Analyze Ashby data, interview processes, pass-through rates, stage timing, and interviewer load; maintain data quality, dashboards, documentation, and self-service tools. Lead cross-functional initiatives, secure executive buy-in, improve recruiting processes, and build AI-powered workflows using Claude. Balance strategic programs with day-to-day support for recruiters and leadership.