Responsibilities
- Contribute to the core platform infrastructure including 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 support model API products, such as request routing, rate limiting, and multi-tenant isolation, under the guidance of senior engineers.
- Build and maintain pieces of data pipelines (ETL/ELT) for API logs, usage analytics, and billing, focusing 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 context and memory systems for conversational workloads, including retrieval, caching, and integration with vector stores and retrieval pipelines.
- Add observability across the platform with 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.
Requirements
- 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, understanding concepts like concurrency and fault tolerance, 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 willingness to pick up new ones.
- Familiarity with 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, asking good questions, welcoming feedback, and wanting to grow.
Nice to Have
- 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, such as a side project, hackathon build, or open-source contribution you are proud of.