Senior Software Engineer, Applied AI

About The Role
Take2 AI is hiring a Senior Software Engineer, Applied AI with deep experience applying agentic AI solutions with multi-agent / RAG in production to design, build, and scale the AI systmes powering Take2's autonomous healthcare recruiting platform.

You'll own applied-AI work end-to-end — from prototyping and model selection through production reliability — as we scale from thousands to millions of candidate conversations.

This role is high-ownership, hands-on, highly technical, and perfect for someone who thrives in a startup environment, playing a key role from design through production with significant ownership over technical execution and direction.

In Terms of Experience
Required:
6+ years software engineering experience (backend or full stack) with significant ML and Applied AI experience as a part of that
Bachelor’s in Computer Science or Computer Engineering
Have shipped multi-agent AI solutions with real-time inference to live users at scale
Designed and implemented agent orchestration, evaluation frameworks, and related tooling
Shipped production AI applications with real-time inference, integrating ML models into live systems
Built and operated distributed backend systems with monitoring and observability in place
Strong proficiency in Python, JavaScript, Node.js, AWS, Kubernetes, Docker
2+ years at an early to mid-stage startup (Series B or lower)

Preferred:
Experience self-hosting AI/ML models for use in voice AI pipelines, including tuning and optimizing them for performance and reliability
Experience owning and operating distributed systems in high-throughput, streaming, low-latency environments, ensuring scalability, reliability, and performance under real-time constraints
Direct, hands-on experience integrating and optimizing STT, TTS, VAD, and LLMs for real-time voice agents
Scaled and optimized voice pipelines for low latency, high availability, and real-time performance

What You’ll Do
Design and build applied AI-powered features and agentic workflows — tweak systems, use RAG, build intra-agent networks & communication — and ship them to production
Select, integrate, evaluate, and tune models for the right balance of quality, latency, cost, and reliability across voice AI pipelines (STT/TTS/VAD)
Build evaluation, guardrail, and observability systems to ensure AI behavior is measurable and safe at scale
Own the backend services and data/inference pipelines that serve applied-AI features in production
Collaborate closely with product and founding engineering to rapidly prototype and deliver with significant ownership over technical direction