Top Hybrid Software Engineer Jobs in San Francisco, CA

Build end-to-end product features from design through delivery, lead projects and product areas, shape technical systems and engineering practices, make pragmatic scalability decisions, and mentor teammates. The role requires strong frontend or backend expertise with the ability to work across the stack, product intuition, cross-functional communication, and ownership in a growing, mission-driven team.

Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.

Provides hands-on technical leadership for enterprise Data Trust platforms. Designs and operates distributed systems supporting data contracts, metadata, lineage, quality, privacy, access controls, reliability, and semantic context for analytics and AI. Leads batch and streaming capabilities across Snowflake, AWS, and Kafka; establishes observability, SLOs, automated enforcement, and operational rigor. Drives cross-functional architecture and multi-quarter initiatives while mentoring engineers and building reusable platform standards.

Leads software engineering for infrastructure workflow platforms, including architecture, implementation, production operations, roadmaps, agile delivery, mentoring, and AI-enabled engineering capabilities. Develops MCP integrations, agent patterns, evaluation guardrails, data pipelines, and secure, observable services. Requires strong Java and Python expertise, systems design, testing, CI/CD, messaging, SQL, responsible AI practices, and cross-functional leadership.

Design, implement, test, debug, and operate highly available backend services at scale. Collaborate across teams to deliver product solutions, manage system availability and scalability, investigate incidents, and apply best practices for operational excellence and cost management.

Software Engineering Interns join MongoDB product and technology teams for a 12-week summer internship. They collaborate with engineers, learn software development practices, and contribute production-level code to MongoDB products and services. Candidates should be pursuing a computer science or related degree, understand data structures, algorithms, and software design, and be proficient in an object-oriented programming language. Interns work 40 hours weekly in a hybrid office model.

Build and maintain reproducible research infrastructure for evaluating digital-twin and large language model systems. Responsibilities include automating experiments, implementing deterministic measurement pipelines, managing versioned datasets and human-participant data, maintaining research-production alignment, monitoring evaluation validity and compute costs, and supporting documented coding workflows. The role requires strong Python, data pipeline, cloud, concurrency, machine learning, and software engineering skills, with preferred experience in LLM systems, model fine-tuning, inference serving, experiment tracking, and generative agents.

Develop and deploy data solutions using Palantir Foundry and AIP for client engagements. Lead technical teams, coach team members, analyze system interactions, build applications with large datasets, and apply data engineering and analytics to deliver client outcomes. The role also includes managing client relationships, planning and executing projects, encouraging technology adoption, resolving stakeholder conflicts, and driving continuous improvement.

Lead the design, development, and rollout of large-scale integrations for BrazeAI Decisioning Studio. Build resilient data ingestion and event-driven activation pipelines across enterprise systems, supporting arbitrary schemas, multi-region deployment, compliance, and high-volume customer data. Own production systems, observability, incident response, and reliability. Partner with data scientists, ML engineers, and product managers, while mentoring engineers and setting technical direction.

Build and own end-to-end product features for Drata’s third-party risk management platform, including vendor-risk workflows, integrations, and AI-powered assessment experiences. Responsibilities include design, implementation, feature-flagged releases, documentation, UX, production monitoring, bug fixes, and technical debt. Partner closely with Product, exercise independent judgment, communicate clearly, mentor peers, and deliver reliable software using JavaScript/TypeScript, Node.js, NestJS, React, event-driven architecture, and durable workflow orchestration.