Software Engineer - Platform & Application

Team: Engineering

Location: Santa Clara HQ

Commitment: Full-time

Workplace Type: onsite

Responsibilities

Contribute to the core platform infrastructure: the API serving layer, state management, policy enforcement, and execution runtime for agentic workflows — starting with well-scoped components and taking on broader ownership as you grow.
Help build and maintain distributed services that back our model API products, including request routing, rate limiting, and multi-tenant isolation, under the guidance of senior engineers.
Build and maintain pieces of our data pipelines (ETL/ELT) for API logs, usage analytics, and billing, with a focus on data correctness and freshness.
Develop and improve internal SDKs and libraries — writing clean, well-tested code with clear contracts that product teams can rely on.
Support our context and memory systems for conversational workloads: retrieval, caching, and integration with vector stores and retrieval pipelines.
Add observability across the platform — structured logging, tracing, and metrics — and help investigate and resolve reliability issues.
Collaborate with ML and product teams to integrate model serving, voice runtime, and tooling infrastructure, learning how the full stack fits together.

Qualifications

0–2 years of professional software engineering experience (internships, co-ops, and strong personal or open-source projects count), or a recent CS degree with equivalent hands-on work.
Solid programming fundamentals and a genuine interest in backend and distributed systems — you understand concepts like concurrency and fault tolerance and are eager to apply them in production.
Some exposure to building backend services, APIs, or data processing — through work, coursework, or projects.
Proficiency in at least one language such as Python, Go, Java, Rust, or C++, and a willingness to pick up new ones.
Familiarity with the basics of cloud infrastructure (AWS/GCP), containers (Docker/K8s), version control, and CI/CD — or clear enthusiasm to learn them quickly.
Curiosity, strong communication, and a collaborative mindset — you ask good questions, welcome feedback, and want to grow.

Bonus points

Coursework, projects, or internship experience touching LLM serving, retrieval, or agentic systems.
Exposure to data pipeline tools (Kafka/Kinesis, Spark/Flink, Airflow) or agent frameworks.
Experience with real-time media (audio/video streaming) or any latency-sensitive system.
A track record of shipping something end-to-end — a side project, a hackathon build, or an open-source contribution you're proud of.