Senior / Staff Software Engineer (Agentic AI Infrastructure)

Build the Future of Agentic AI | Staff/Senior Engineer | Up to $250K + Equity | High-Growth AI Startup

This Jobot Job is hosted by: Craig Rosecrans
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Salary: $200,000 - $250,000 per year

A bit about us:

We are partnering with an exceptionally exciting, well-funded AI startup that is building next-generation technology to solve complex, high-impact challenges across government, enterprise, and highly regulated industries.

Backed by leading institutional investors and an accomplished founding team with experience spanning major technology companies and senior government institutions, this organization is developing sophisticated AI-powered platforms that fundamentally transform how organizations access intelligence, conduct research, and execute complex workflows.

This is an opportunity to join an ambitious startup at a pivotal stage of growth, working directly with the CTO and founding team to architect and build the core infrastructure powering its agentic AI platform.

Unlike traditional software engineering roles focused on maintaining existing products, this position offers tremendous technical ownership, architectural influence, and the opportunity to build production-grade autonomous AI systems from the ground up.

Why join us?

Exceptional Technical Ownership: Own foundational AI infrastructure rather than a small component of an established platform. Your architectural decisions will directly influence the company's technology and future products.

Direct Access to Leadership: Report directly to the CTO and collaborate closely with an accomplished founding team.

Frontier Agentic AI Development: Work on genuinely complex AI engineering challenges involving autonomous agents, orchestration, reasoning, persistent memory, secure tool execution, and production reliability.

Meaningful Equity & Startup Upside: Join a funded, high-potential AI startup at an early stage, with an opportunity to participate in the company's long-term success.

High-Impact Applications: Build technology addressing sophisticated challenges across government, enterprise, and other mission-critical environments.

Builder-Focused Engineering Culture: Work alongside ambitious, technically accomplished professionals who value ownership, execution, collaboration, and solving difficult problems.

Compensation & Work Environment

Base Salary: Up to $250,000 annually, with flexibility for exceptional candidates.

Equity: Meaningful equity participation.

Location: SoHo, New York City.

Onsite Requirement: Five days per week, with 13 remote days available per quarter.

Employment: Full-time, direct hire.

This is an in-person, highly collaborative startup environment designed for engineers who thrive when working directly with leadership, moving quickly, and building innovative technology from the ground up.

If you are passionate about agentic AI, enjoy solving technically challenging problems, and want the opportunity to make a meaningful impact on the future of autonomous AI systems, we would love to connect.

Job Details

tems, asynchronous execution, state management, APIs, and fault-tolerant application design.

Experience with retrieval-augmented generation (RAG), context management, or knowledge retrieval systems.

Understanding of the challenges associated with nondeterministic AI behavior, including reliability, evaluation, observability, and failure recovery.

Ability to independently own complex engineering initiatives, make architectural decisions, and deliver production-ready solutions.

Comfortable working in a fast-moving, early-stage startup environment with significant individual ownership.

Preferred Qualifications

Experience with agent orchestration technologies such as LangGraph, LangChain, AutoGen, CrewAI, or custom-built agent frameworks.

Experience building persistent agent memory, multi-agent coordination, or long-running autonomous workflows.

Experience developing AI evaluation frameworks, tracing infrastructure, or model observability platforms.

Familiarity with multi-provider LLM architectures, inference optimization, and model-routing strategies.

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