Staff Software Engineer - Federal

Description

This Staff Software Engineer role is hybrid and based in Santa Clara, CA, with 2 days per week in the Santa Clara office.

The role includes support for the company's US regulated markets.

You will lead the design, build, and delivery of complex AI-native and cloud-native systems on your own. As an IC4, you own key components or features, set technical direction in your area, and act as a go-to technical resource for peers and junior engineers. The job combines strong software engineering fundamentals with hands-on work in AI/ML integration, cloud architecture, and production systems. You will balance deep technical work with broader impact, work across teams on unclear problems, and push platform improvements forward.

Responsibilities

• Lead the design and architecture of production-critical AI-native systems, such as LLM integrations, agent workflows, retrieval pipelines, and inference services.
• Own medium-to-large features or systems from specification through design, implementation, testing, deployment, and production monitoring.
• Weigh trade-offs between latency, cost, scalability, reliability, and model quality, and make sound architecture decisions.
• Set up evaluation frameworks, test strategies, and production monitoring for AI features, and help the team adopt eval-driven development.
• Design and tune high-performance data pipelines, embedding systems, and vector stores that handle millions of rows and complex retrieval.
• Apply security practices such as prompt-injection defense, data privacy, PII handling, responsible AI guardrails, and regulatory compliance.
• Take part in cross-team architecture reviews and help shape platform-level technical decisions.
• Track emerging AI/ML frameworks, services, and techniques, and recommend the ones worth adopting.
• Mentor junior and mid-level engineers (IC2/IC3) through pair programming, design reviews, architecture talks, and hands-on coaching.
• Run thorough code reviews, including checks on prompts, agent logic, retrieval strategies, and model behavior.
• Find technical debt and improvement opportunities, and lead efforts to fix them.
• Share knowledge through documentation, brown-bag sessions, and architecture guidance, and help onboard new team members.
• Help interview and assess engineering candidates.
• Set an example in craftsmanship, testing discipline, and debugging.
• Work with product management, domain specialists, and other engineering teams to define requirements and technical specs.
• Explain complex technical concepts and AI trade-offs to non-technical stakeholders such as product, leadership, and customers.
• Lead root cause analysis for customer-reported problems, including model behavior, hallucinations, retrieval quality, and distributed system faults.
• Communicate clearly about design choices, implementation challenges, AI limits, and production incidents.
• Build relationships with adjacent teams and external partners where useful.
• Look for ways to improve development processes, tooling, and team efficiency.
• Write robust, maintainable, well-tested AI-native software in Python, Java, and JavaScript as fits the task.
• Build unit tests, integration tests, and AI evaluation harnesses covering accuracy, regression, and hallucination detection.
• Troubleshoot issues across distributed systems, probabilistic models, and cloud infrastructure.
• Apply secure coding and responsible AI guardrails throughout development.
• Manage deployment, monitoring, and iteration in production, and respond to performance problems and model drift.
• Work with AWS, GCP, or Azure, managed AI services such as Bedrock, Vertex AI, and Azure OpenAI, Kubernetes, and CI/CD pipelines.
• Help maintain code and architecture standards.
• Design and build custom the company applications, extensions, and integrations.
• Write workflows, business rules, and automation using the company scripting (JavaScript, GlideScript, REST APIs).
• Build data models, forms, and dashboards for the company apps, and tune database queries for speed.
• Connect third-party AI/ML services and custom APIs to the company platform services.
• Help build and maintain the company plugin and app architecture, following platform best practices and coding standards.
• Take part in the company upgrades and configuration management, and keep solutions compatible with platform updates.
• Mentor junior engineers on the company capabilities and development patterns.
• Take part in triage and root cause analysis for production the company application issues.
• Work with customer success and support teams to resolve complex technical problems.
• Advise customers on application use, configuration, and troubleshooting.
• Spot patterns in customer issues and drive fixes that prevent them from returning.
• Contribute to customer documentation and internal knowledge bases.
• Support escalations and resolve critical issues that affect customer operations quickly.
• Collect customer feedback and pass product improvement ideas to product management.

Requirements

• 8+ years of software development experience with a bachelor's degree, 6+ years with a master's degree, 3+ years with a PhD, or equivalent work experience.
• Practical software development experience, including 5+ years with modern cloud-native and AI-native systems.
• 3+ years of hands-on work with LLM prompt engineering, agent design, and retrieval systems such as RAG, semantic search, and knowledge bases.
• 3+ years of proven experience integrating third-party AI/ML services and platform APIs, such as OpenAI, Anthropic, AWS Bedrock, and Google Vertex AI.
• 2+ years of hands-on experience building applications and extensions on the company platform.
• Strong skills in JavaScript/GlideScript, Python, and Java, plus familiarity with the company scripting patterns and APIs.
• Solid grasp of the company data model, form and workflow design, and business process automation.
• Solid understanding of microservices, REST and streaming APIs, and modern backend frameworks such as Spring Boot and FastAPI.
• Experience designing and tuning high-performance data pipelines, embedding systems, and vector stores at scale.
• Demonstrated expertise with LLM orchestration frameworks such as LangChain, LlamaIndex, and Semantic Kernel, and vector databases such as Pinecone, Weaviate, and pgvector.
• Experience with cloud platforms and managed AI services on AWS, GCP, or Azure, and hands-on work with infrastructure-as-code.
• Proficiency with Kubernetes, containers, CI/CD pipelines, and automated testing frameworks.
• Experience building and debugging evaluation harnesses, benchmarks, and metrics for AI/ML features.
• Proven ability to find and isolate root causes in complex, distributed, and probabilistic systems.
• Strong communication skills for both technical and non-technical audiences. Customer-facing experience is a plus.
• Experience with Agile and Scrum, and comfort with iterative development and continuous improvement.
• Demonstrated ability to engage with customers, understand their needs, and help resolve issues.
• A record of owning significant technical initiatives and delivering them.
• Demonstrated mentorship of junior engineers or technical leadership in previous roles.

Conditions

Hybrid role based in Santa Clara, CA, with 2 days per week in the Santa Clara office.

Supports US regulated markets.

Base pay for this location is $176,100 - $308,200, plus equity where applicable, variable or incentive pay, and benefits. Actual pay depends on qualifications, skill level, competencies, and work location, and the base shown is a guideline. Pay is based on the role's geographic location and may change with work location.

Work persona is flexible. The company may confirm the distance between your home and the nearest office with a third-party service to determine eligibility.

Benefits

• Health plans, including flexible spending accounts.
• 401(k) plan with company match.
• ESPP.
• Matching donations.
• Flexible time away and family leave programs.