Senior Software Engineer, Agentic Infrastructure

Handshake•San Francisco, CA

5d•$208,000 - $260,000•Hybrid

About The Position

Handshake is hiring a Senior Software Engineer to join Backend Platform, with an initial focus on supporting Handshake AI (HAI) and internal AI initiatives. You will build and maintain backend services, shared platform capabilities, and developer tooling that support HAI and internal AI initiatives. The work centers on core backend platform concerns—service architecture, APIs, data systems, performance, reliability, and developer experience—in an area making increasing use of LLMs and agents. We are looking for someone who combines strong backend fundamentals with practical knowledge of LLM-powered and agentic systems.

Requirements

5+ years of professional software engineering experience, primarily in backend or platform engineering.
Experience designing, building, and operating production services and APIs.
Strong understanding of distributed systems, data modeling, service boundaries, common backend architecture patterns, and reliability and performance tradeoffs.
Deep TypeScript and Next.js experience.
Strong experience with relational databases, including schema design, query optimization, indexing, transactions, and diagnosing database-related performance problems. Experience with PostgreSQL is especially relevant.
Familiarity with backend technologies such as Temporal, Redis, Elasticsearch, GraphQL, or event-driven systems.
Experience operating services in cloud and containerized environments. Our broader platform uses technologies including GCP, Kubernetes, Terraform, and Docker.
Practical knowledge of LLM-powered systems, including concepts such as model and tool integration, RAG, agent orchestration, prompt management, evaluation, and observability.
A record of independently driving ambiguous projects from problem definition through production rollout.
Strong communication skills and the ability to collaborate with product engineers and other teams across Developer Platform.
Sound technical judgment and a preference for understandable, maintainable systems over unnecessary complexity.

Nice To Haves

Direct production experience with product features powered by ML, LLMs, or agents.
Experience with LLM orchestration frameworks or agent architectures involving tool use, memory, and multi-step workflows.
Experience building internal AI tools or shared AI capabilities used by other engineers.
Experience creating internal platforms, paved paths, or self-service tools used by other engineers.
Experience modernizing mature backend systems while continuing to support active product development.
Experience with event-driven architecture or high-throughput data systems.

Responsibilities

Design, build, and operate backend services and APIs that support HAI products and internal AI initiatives, primarily within our full-stack TypeScript repository.
Develop shared libraries, services, and architectural patterns that reduce duplicated work and make backend systems easier to extend and operate.
Improve the performance, scalability, reliability, and observability of HAI's backend systems.
Lead work involving database design, data access patterns, query performance, indexing, caching, and the evolution of data models as products grow.
Identify recurring product-engineering problems and turn the right ones into reusable platform capabilities and well-understood patterns.
Make sound architectural decisions across mature systems and new services, balancing delivery speed, performance, maintainability, and operational cost.
Contribute directly to internal AI initiatives, including shared capabilities used to build, evaluate, and operate LLM-powered and agentic systems.
Partner with other teams in our Developer Platform group, including ML and Cloud Engineering, on model and service integration, orchestration, evaluation, observability, and failure handling.
Participate in technical planning, design and code reviews, operational support, and incident response.
Mentor other engineers and help establish practical backend engineering standards across HAI and internal AI initiatives.