> Markdown version of [/jobs/ext/3870203-software-engineer-platform-application](https://www.wearedevelopers.com/jobs/ext/3870203-software-engineer-platform-application). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Software Engineer - Platform & Application

- **Company:** Boson AI USA Inc.
- **Location:** Santa Clara, CA, United States
- **Experience:** Starter
- **Contract:** Internship / Graduate position
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Airflow, Amazon Web Services, C++ (Programming Language), Cloud Computing, Continuous Integration, Extract Transform Load (ETL), Distributed Systems, Fault Tolerance, Python (Programming Language), Open Source Technology, Software Engineering, Rust (Programming Language), Data Logging, Data Processing, Retrieval-Augmented Generation, Large Language Models, Apache Spark, Caching, Backend, Agentic-AI, Rate Limiting, Kubernetes, Apache Flink, Apache Kafka, Machine Learning Operations, Video Streaming, Software Version Control, Data Pipelines, Api Management, Docker, Golang
- **Published:** October 22, 2026
- **Apply:** https://www.juju.com/job/16_439323c83

## About the Role

* 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.

## Description

About the Role: Help build and operate the core platform behind Boson's model APIs and agentic products. You'll work alongside senior engineers on the infrastructure that every Boson agent runs on - API serving, state management, data pipelines, context retrieval, and execution runtime - shipping real features while growing into deeper ownership over time. This is a role for someone early in their career who wants to learn distributed systems by building them, with strong mentorship and meaningful production responsibility from day one.
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.