Senior Software Engineer, Applied AI

About Take2 AI

Take2 is an AI agents platform purpose-built for healthcare recruiting. Take2’s agent network automates the end-to-end recruiting process, from sourcing and screening to credential verification, scheduling, and onboarding.

We work with enterprise healthcare customers, including some of the largest health systems in the USA.

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