AI Jobs in San Francisco

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Design and deploy AI solutions by working closely with customers to translate their challenges into functional agents, integrating APIs and data sources to automate real business processes. Prototype quickly, build the first version, get it into production, and refine based on real-world feedback. Collaborate with customer teams across engineering, product, and operations to ensure the agent performs, scales, and delivers measurable outcomes. Own end-to-end delivery from discovery call to deployment, leading the technical build, testing, and iteration to ensure the experience feels natural, human, and on-brand. Drive adoption and expansion by sharing results, training teams, and embedding within the customer organization to uncover new opportunities for automation and scale. Act as the face of Bland by being the customer’s champion, traveling on-site, developing real relationships with customers and stakeholders, hosting training sessions and dinners, and providing unreasonable hospitality.

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As Product Manager for Ghostwriter, you will define how humans interact with software in the agent era, owning the end-to-end product experience from prompt to agent to outcome and helping scale Sierra to thousands of customers. You will define how AI augments agent development by shaping the workflows by which CX teams and developers draft journeys, run simulations, analyze conversations, and improve agents using natural language. You will balance autonomy and control by designing the appropriate human-in-the-loop patterns such as approval flows, change review, and workspace isolation to ensure customer trust in Ghostwriter's changes to their agents. You will partner closely with AI/ML and platform engineering teams to collaborate on model selection, harness engineering, execution architecture, and evaluation/testing infrastructure. Additionally, you will act as the voice of the agent builder by deeply understanding the pain points of CX managers, agent developers, and technical teams configuring journeys, integrations, and simulations.

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As a Software Engineer on the AI Voice Agent team, you will work on real-time speech pipeline systems including live audio buffering, streaming, latency optimization, and integrating with speech providers. You will build and improve conversation intelligence systems that manage the LLM layer for natural conversation flow, including prompt construction, context management, function calling, and dialogue management. You will develop the action framework that allows the AI Voice Agent to execute tasks during calls such as querying account data, creating tickets, and checking order status, handling API configuration, success/failure branching, authentication management, and runtime execution. Additionally, you will work on knowledge ingestion, storage, and retrieval for the voice agent and manage memory for retaining information across conversations to improve responses. You will collaborate with designers to create easy-to-use interfaces for agent lifecycle management including creation, configuration, testing, and deployment. You will contribute to building evaluation frameworks and metrics for voice AI quality, post-call analytics, and instrumentation, as well as participate in the on-call rotation.

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As a Software Engineer on the AI Voice Agent team, you will work on real-time systems involving live audio such as buffering, streaming, and latency optimization, along with integrating speech providers. You will build and improve conversation intelligence systems, including prompt construction, context management, function calling, and dialogue management to make conversations feel natural. You will develop the action framework to execute configurable API calls, manage success/failure branching, authentication, and runtime execution during calls. You will work on knowledge ingestion, storage, retrieval, memory, and context for the voice agent to improve its performance over time. Additionally, you will collaborate on agent lifecycle tasks such as creation, configuration, testing, and deployment of voice agents and help build evaluation frameworks for model performance, call quality metrics, and call analytics. Participation in on-call rotations is also expected.

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As a Senior Software Engineer on the AI Voice Agent team, you will work on real-time systems involving live audio streaming and latency optimization integrated with speech providers. You will build and improve conversation intelligence systems that manage LLM layers, including prompt construction, context management, function calling, and dialogue management to create natural, actionable phone conversations. You will develop the action framework allowing configurable API calls with branching logic and runtime execution, supporting tasks like data lookup and ticket creation during calls. You'll manage knowledge ingestion, storage, and retrieval to enhance agent memory and learning over time. You will collaborate with designers to enable customers to create, configure, test, and deploy voice agents through intuitive product experiences. Additionally, you will help develop evaluation frameworks, analytics, call quality metrics, and monitoring instrumentation, and participate in on-call rotation duties.

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As a Staff Software Engineer in the Foundations department, responsibilities include leading the design and implementation of highly scalable systems for the Managed AI offerings, driving the long-term technical roadmap for the Foundations team to support growth and evolving AI workloads, working cross-functionally with Cloud Engineering to align technical goals and solve integration challenges, leading by example through high-quality code contributions and mentoring Senior and Staff-level engineers, championing reliability, observability, and performance by identifying and resolving systemic bottlenecks, and staying current with AI infrastructure trends to ensure efficient and powerful tools are utilized.

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Design, automate, maintain, and optimize Python ETL pipelines (Spark/Ray) for large-scale multimodal data. Build and maintain data cataloging, lineage, quality tooling, integrity verification, access controls, and lifecycle management systems. Provide guidance, internal tools, and documentation to colleagues on data best practices. Serve as a custodian of the company’s datasets, ensuring overall data health, quality, and discoverability.

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As the Lead Data Scientist, you will set the data strategy by defining what is measured, how it is measured, and establishing the metrics architecture that connects product usage, retention, monetization, and growth across the company. You will transform the data team into a product team by building internal data products and self-serve AI interfaces, automated reports, and tools for non-technical stakeholders. You will build the semantic layer, documentation, and context infrastructure to make the data warehouse AI-readable and accurate. Additionally, you will build AI-powered systems and automated pipelines to replace manual work within the data lifecycle, including dbt model generation, data quality monitoring, experiment analysis, and insight delivery. You will own product analytics and experimentation by partnering with Product, Engineering, and Design to design experiments, interpret results, and provide insights that guide product decisions. Your responsibilities also include driving growth and business intelligence by maintaining and evolving dashboards and reporting for Sales, Marketing, Customer Success, and leadership, ensuring metrics are visible, trusted, and actionable. Finally, you will scale the data team’s output through systems and AI-powered tooling to support company growth without increasing headcount linearly.

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Use machine learning expertise to create domain-relevant questions and review AI-generated responses for accuracy, rigor, and relevance to real-world physics research and practice.

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Lead, hire, and mentor a high-performing team of AI Deployment Engineers supporting Codex customers across strategic accounts. Own the operating model and engagement strategy for Codex deployment efforts, ensuring customers successfully move from pilot to production adoption. Guide teams in designing and implementing AI-enhanced development workflows, automations, and scalable deployment architectures. Act as the senior technical escalation point for complex customer implementations and deployment challenges. Partner with Sales, Product, Research, and Applied Engineering teams to align customer outcomes with product direction and roadmap priorities. Help establish repeatable deployment playbooks, technical patterns, and best practices that enable scaled adoption of AI coding tools. Coach engineers to operate as trusted advisors to engineering leadership and executive stakeholders. Synthesize insights from customer deployments and translate them into actionable feedback for internal teams. Champion safe, reliable, and effective adoption of AI-powered development workflows across industries.