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# Software Engineer

- **Company:** Acceler8 Talent
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Computer Programming, Software Debugging, Programming Tools, Experimental Data, Python (Programming Language), Scientific Computating, Cloud Platform System, Large Language Models, Multi-Agent Systems, Kubernetes, Virtual Agents, Data Pipelines, Docker
- **Published:** November 8, 2026
- **Apply:** https://www.juju.com/job/28_25303_60fa1d0effdbaf71fd2518b66cdb56fd

## About the Role

* Experience building agentic systems or workflows in production (0*1 experience strongly valued).
 * Strong Python experience, including async programming and modern AI tooling.
 * Experience building tools or APIs that agents can reliably reason about and call.
 * Comfort working on systems that operate under real constraints and failure modes.
 * Motivation to apply AI to hard scientific and technical problems., * Experience building SDKs or developer tools.
 * Exposure to scientific computing, simulation pipelines, or ML-in-the-loop systems.
 * Experience with Docker, Kubernetes, or hybrid on-prem/cloud environments.

## Description

Our core product is an AI agent that plans, executes, and iterates across complex scientific workflows - running optimization loops, querying literature and experimental data, and scheduling large-scale simulations. This system operates continuously and directly influences real-world outcomes., * Build and maintain agentic tools that retrieve data, call APIs, write and execute code, and orchestrate scientific workflows.

 * Design and debug agentic workflows (single- and multi-agent), including planning, goal decomposition, and context management.
 * Develop data pipelines that ingest, transform, and serve data to LLM-powered systems.
 * Build production-quality asynchronous Python systems with strong typing, packaging, and testing discipline.
 * Instrument systems with telemetry and observability so failures are diagnosable and recoverable.
 * Work closely with ML engineers, scientists, and automation teams in a deeply technical, on-site environment.