Full-Stack ML Systems Engineer

Job Description

Job Description

About Us

We are an elite, research-backed AI infrastructure startup building the defining workflow intelligence and policy management layer for enterprise multi-agent applications. While the market has flooded with tools to build simple AI agents, nobody has cracked the infrastructure gap required to scale them efficiently, safely, and cost-effectively in production. We bridge this exact gap by applying deep systems programming, software-defined networking, and OS-level primitives to AI workflow orchestration.

We recently closed a $5M Seed round and operate as a flat, hyper-technical team of three. Our leadership includes:

• The CEO: A 3x deep-tech founder (PhD, ex-Bell Labs, ex-Qualcomm) who successfully scaled his last venture from Seed to Series B.
• The Co-Founder: A Regents Chair Professor at UT Austin and world-renowned ML systems researcher with a pedigree spanning CMU and Stanford.

Our core technology originates from a top-tier academic research group. This is not another API wrapper—this is a foundational infrastructure play designed for the next frontier of enterprise software.

What You'll Be Doing

• Bridge Research & Production: Work directly alongside our Chief Architect and CEO to translate cutting-edge ML systems research into an enterprise platform that product developers love to use.
• Build the Orchestration Fabric: Design and implement full-stack ML systems for multi-agent workflow orchestration, merging deep backend systems capability with clean, satisfying developer interfaces.
• Architect the Scaling Layer: Develop the policy-driven scaling mechanics that govern multi-agent interactions, resolving the infrastructure bottlenecks that limit agentic autonomy at scale.
• Own the Developer Experience: Take research-grade ideas and make them legible by authoring technical documentation, code examples, sample applications, and intuitive onboarding flows.
• Systems Integration: Collaborate across distributed systems design, networking configurations, and asynchronous event streams applied directly to runtime AI operations.

What We're Looking ForExperience & Seniority

• 5+ years of production experience engineering ML systems, OR a PhD from a top-tier institution in a relevant ML Systems/Distributed Computing field.

Core Technical Competencies (Critical)

• Hands-on Agentic Frameworks: Proven experience building and deploying multi-agent applications to live production environments using frameworks like LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, ADK, or custom orchestration systems.
• Production Scaling & Compute: Direct exposure handling scale on either the orchestration side (managing complex state, cyclical routing, and memory loops) or the infrastructure side (scaling AI compute fabrics, asynchronous jobs, queues, and container clusters).
• Model-Serving Platforms: Practical production experience deploying and optimizing models via frameworks such as vLLM, SGLang, Ray, NVIDIA Triton, or NVIDIA Dynamo.
• Full-Stack Implementation: Strong Python background combined with full-stack capabilities, including constructing front-end visual telemetry dashboards (using Grafana, Tableau, Streamlit, or similar).
• Open Source Focus: Experience utilizing, developing upon, or contributing directly to open-source software repositories.

Mindset & Soft Skills

• Enterprise Product Instincts: The ability to look at powerful backend capabilities and figure out how to make them readable, structured, and satisfying for enterprise end-users.
• Thriving in Ambiguity: An early-stage startup mentality—comfortable shipping fast, iterative code within dynamic, shifting, and initially ill-defined parameters.
• Clear Communication: Strong written skills to help transform complex technical primitives into clear developer documentation and clean product language.
• A background as a Solutions Architect or Forward Deployed Engineer is a significant plus.
  Why You Should Join Us
  
  • Solve an Unsolved Problem: Nobody has cracked the gap between building a toy agent app and scaling it across a global enterprise. You will build technology that fundamentally does not exist anywhere else.
  • Unmatched Pedigree: Learn from and build with a world-class founding team combining serial entrepreneurship with elite academic systems research.
  • Founding-Level Impact: As Employee #4, your code dictates the core blueprint of the platform. You get a direct up-to-1% equity block in a venture backed by a highly technical $5M validation check.