Backend Engineer

New York City / San Francisco / Remote /
Engineering /
Gauntlet’s mission is to help make blockchain protocols and smart contracts safer and more trustworthy for users. Building decentralized systems creates new challenges for protocol developers, smart contract developers, and asset holders that are not seen in traditional development and investing. Gauntlet is building a blockchain simulation and testing platform that leverages battle tested techniques from other industries to emulate interactions in crypto networks. Simulation provides transparency and greatly reduces the cost of experimentation so that teams can rapidly design, launch, and scale new decentralized systems.

Our goal is to make building an agent-based simulation for blockchains and smart-contracts as streamlined as possible. At the core of our infrastructure stack is a blazing fast C++ engine that allows us to simulate protocols, contracts, and network interactions millions of times faster than they run in reality.

You will be working as part of an experienced team that has developed simulation software for many other industries, including high-frequency trading, autonomous vehicles and ride-sharing, and the natural sciences.


    • Automate and scale simulation model deployment on cloud infrastructure
    • Developing scalable ETL pipelines for analyzing public blockchain data and simulation results
    • Design and implement a Python SDK that interfaces with the C++ simulation engine
    • Building static analysis tools and agent-based simulations for smart contracts


    • Experience developing production quality software in Python, Rust, Go, C++, or other high-performance languages
    • Experience building and scaling applications on public cloud infrastructure (Docker, AWS/GCP, Kubernetes, Mesos, etc..)
    • Experience with distributed computation frameworks and related technologies such as Kubeflow, Spark, TensorFlow, Flink, etc.

Bonus Points

    • Smart contract development experience (e.g. Solidity)
    • Experience with building machine learning infrastructure at scale