Software Engineer, Backend (New Grad)

Otter.ai•Mountain View, CA

23h•Hybrid

About The Position

We're building the next generation of AI-native product experiences, where conversations turn directly into outcomes. As a Software Engineer on our Product teams, you'll work at the intersection of product, AI, and systems to shape how users interact with intelligence in real workflows. You'll work across the full lifecycle of AI-driven features, from data ingestion and model orchestration to output structuring and delivery into user-facing product surfaces. This role requires balancing product thinking with systems design, ensuring that AI outputs are not only intelligent, but also reliable, performant, and aligned with real user needs. You'll partner closely with product, design, and AI teams to rapidly prototype, ship, and iterate on experiences like summaries, action items, and intelligent workflows — turning raw model output into high-quality product features.

Requirements

Has a bachelor's or master's degree in Computer Science or a related field
Is a recent grad or has up to 1 year of experience building software, with a growing sense of how backend decisions impact user experience
Writes clean, maintainable code (Python preferred) and is comfortable working across a modern web stack (APIs, data systems, async workflows)
Thinks in terms of product outcomes, not just technical implementation — cares about whether what they build is actually useful to users
Is excited about working with AI/LLM-powered systems, and is interested in shaping their behavior in real-world applications
Takes ownership of problems end-to-end and collaborates well across product, design, and AI disciplines
Is comfortable operating in ambiguous, fast-moving environments and can iterate quickly on both product and technical direction

Nice To Haves

Any experience (including coursework, projects, or internships) building or experimenting with LLM-powered features (prompting, evaluation, RAG, etc.)
Prior internship or project experience building product-facing systems

Responsibilities

Build AI-native product experiences end-to-end
Design and implement features that transform model output into structured, actionable user value—such as summaries, action items, and intelligent workflows.
Own the product quality loop: Continuously improve output quality by iterating on prompts, data flows, and backend logic, using real user feedback, evaluations, and experimentation.
Bridge product and AI systems: Translate product requirements into systems that orchestrate LLMs, retrieval, and structured data—shaping how intelligence behaves in production, not just integrating it.
Design for real-world constraints: Make deliberate tradeoffs across latency, cost, reliability, and output quality to deliver fast, trustworthy experiences at scale.
Prototype and ship at high velocity using AI-assisted development: Leverage state-of-the-art tools (e.g., coding copilots, LLM-driven workflows) to move quickly from idea to production while maintaining high engineering quality.
Build clean, scalable backend systems: Develop services in Python (e.g., Django) that support dynamic, AI-driven product experiences and evolving system requirements.