• Help redefine the engineering craft by integrating AI in our development lifecycle. • Make use of the latest AI tools for building and shipping our next product hit. • Work on bold, AI-first product features from ideation to launch, solving real-world problems for our users. • Partner with experienced engineers, product managers, and designers to bring new ideas to life. • Be a key contributor in an environment that values continuous learning, curiosity, and creative problem-solving.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We’re looking for a Senior Software Engineer with strong backend fundamentals who is excited about building production AI systems. This role is a great fit for a backend SWE who works comfortably in AI-driven problem spaces and wants to apply strong software engineering skills to create LLM-backed products and platforms. This is not a research role. While we work closely with research and science teams and often operate in greenfield spaces, this position sits squarely in a product engineering organization. The focus is on designing, building, and operating reliable systems that ship real value to customers and internal users. Engineers on this team may lean more toward platform foundations (core services, APIs, enablement) or toward AI application and evaluation work (LLM-powered features, agentic workflows, quality systems), depending on strengths and interests. What unites the role is a strong commitment to software engineering rigor, stewardship, and continuous learning. Our team places a high value on experimentation and knowledge sharing. Engineers are encouraged to explore new AI tools and techniques to improve their own productivity and the quality of their work, and to share what they learn with others. As a Senior AI Software Engineer, you will: - Design, build, and operate backend services in Python that power AI-driven products and shared capabilities. - Build and maintain resilient service integrations across internal and external systems, handling failure modes, rate limits, and interface changes. - Own ambiguous, end-to-end problems: from early design and architecture through implementation, rollout, and iteration in production. - Develop and evolve LLM-backed features and agentic workflows used in production, with a focus on reliability, observability, and real-world behavior. - Contribute to core AI platforms and enablement systems—services that your team uses directly and that other engineers can build on. - Collaborate with product managers, researchers, and other engineers across teams to turn loosely defined AI use cases into concrete, production-ready systems. - Help shape evaluation and quality strategies for AI-powered features, including building or extending evaluation harnesses, benchmarks, or feedback loops. - Act as a technical leader for the work you own—making sound design decisions, coordinating with stakeholders, and contributing to design and code reviews. Qualifications - A degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience. - 4+ years of experience building and shipping production backend software (Senior or Staff level, depending on experience and impact). - Excellent modern Python engineering skills, with the ability to work effectively in distributed, asynchronous environments. - Strong system design skills, including the ability to write clear design docs and solution specifications that align stakeholders and drive sound architectural decisions. - Demonstrated ability to shepherd work from concept through production in complex, evolving environments. - Proven experience designing and implementing integrations across multiple systems in production environments. - Hands-on experience shipping and operating LLM- or generative-AI–backed features or services as part of larger production systems. - Excellent communication skills and a collaborative approach in globally distributed teams. Requirements - Experience designing or operating core platforms, internal services, or developer enablement tooling. - Experience with applied LLM systems and context engineering, including agentic workflows, retrieval-augmented generation, or evaluation pipelines. - Experience building and evolving service interfaces using REST, GraphQL, and/or gRPC, with an understanding of tradeoffs around performance, schema evolution, and compatibility. - Experience with stateless and stateful backend systems (e.g., relational or NoSQL databases, caching, streaming systems). - Experience with modern AI engineering ecosystems, such as Pydantic AI, LLM tracing and observability systems, evaluation pipelines, or MCP. - Familiarity with MLOps or AIOps concepts and tooling (e.g., MLflow, Databricks, model gateways). - Experience with cloud infrastructure (AWS, Azure, GCP), including effective use of managed services, and deployment tools (Docker, Kubernetes, Terraform, ArgoCD). - Background in—or curiosity about—applying AI to cybersecurity or similarly complex, real-world domains. Benefits - Medical, Vision, Dental, 401(k), Commuter, Health and Dependent FSA - Unlimited PTO - Industry-leading gender-neutral parental leave - Paid Company Holidays - Paid Sick Time - Employee stock purchase program - Disability and life insurance - Employee assistance program - Gym membership reimbursement - Cell phone reimbursement - Numerous company-sponsored events, including regular happy hours and team-building events

• Lead the technical design and implementation of AI-powered product features from concept through production. • Own end-to-end architecture for AI solutions, including data flows, model integration, APIs, and application integration. • Make pragmatic decisions to accelerate delivery while maintaining system integrity. • Ensure AI solutions are secure, observable, scalable, and aligned with platform standards. • Act as the technical lead for a cross-functional AI Pod. • Break down product requirements into executable technical workstreams and prototypes. • Guide rapid iteration cycles, proofs-of-concept, and MVPs, balancing experimentation with production readiness. • Review code, architecture, and technical decisions to maintain quality and velocity.

• Design and implement user-facing AI features across frontend and backend systems. • Build and evolve APIs and services that integrate AI models, data pipelines, and product workflows. • Translate product requirements and UX designs into high-quality, maintainable code. • Rapidly prototype and iterate on AI-driven experiences based on user feedback and experimentation results. • Implement intuitive user interfaces that expose AI capabilities clearly and responsibly. • Collaborate with Product and Design to shape AI interactions, prompts, and workflows. • Handle edge cases, fallbacks, and user feedback when AI outputs are uncertain or degraded. • Ensure UI performance, accessibility, and responsiveness. • Develop backend services that orchestrate AI calls, business logic, and data access. • Integrate with internal APIs, data services, and third-party AI platforms. • Implement secure, scalable request handling, including latency and cost considerations for AI usage. • Support feature flags, configuration, and experimentation frameworks. • Write automated tests across frontend and backend components. • Implement logging, monitoring, and basic performance tracking for AI-enabled features. • Participate in code reviews and adhere to team engineering standards. • Help diagnose and resolve production issues related to AI behavior, data, or integrations. • Work closely with the AI Pod Tech Lead to execute technical designs and standards. • Partner with data scientists and ML engineers to integrate models into product features. • Contribute ideas for improving AI usability, reliability, and developer productivity. • Share learnings and patterns across the AI Pod.