Job description

About Boson AI: At Boson AI, we are not just building AI solutions; we are pioneering the future of enterprise AI. Driven by a passion for cutting-edge AI research, particularly in the transformative areas of large language models and agentic systems, our mission is to tackle the most complex real-world problems for businesses and unlock significant value. We are a dynamic and collaborative team of researchers and engineers who thrive on pushing the boundaries of what's possible, dedicated to delivering high-quality, reliable products that seamlessly integrate into the fabric of enterprise workflows and set new industry standards. 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. 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.

Requirements

Must-haves

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.
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.

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiapimlpythongojavarustawsgcpdockerci/cdllm

Platform: lever
Effort: quick