Hume AI

Senior Software Engineer - Backend & Machine Learning

Hume AI
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2 months ago

Base Salary

$180k - $250k/yr

Responsibilities

  • Integrate cutting-edge AI models into services and toolkits for researchers and developers.
  • Build backend applications, cloud infrastructure, and ML lifecycle capabilities.
  • Build and deploy models for inference services at scale.
  • Develop evaluation tools and support model evaluation and deployment.
  • Collaborate with research scientists and engineers to add new capabilities to the Hume platform.

Requirements

  • Expertise in the Python ecosystem and popular machine learning libraries and tools, including examples such as PyTorch, JAX, TensorFlow, XGBoost, scikit-learn, pandas, and NumPy.
  • Experience building and deploying models for inference services.
  • Experience writing backend services in multiple languages, such as Kotlin, Go, Rust, Java, and C++.
  • Understanding of core machine learning concepts, including model architecture, training, and evaluation.
  • Expertise working with storage and compute on a cloud platform such as Google Cloud or AWS.
  • Excellent communication and collaboration skills.
  • Familiarity with data engineering principles or building large-scale data pipelines is a bonus.
  • Familiarity with building and deploying LLM-integrated products is a bonus.
  • Experience at the intersection of machine learning research and engineering is a bonus.
  • Understanding of modern cloud deployment strategies is a bonus.
  • Experience using service deployment tooling such as Kubernetes, Helm, Docker, or Argo is a bonus.

Tech Stack

Argo CDAWSC++DockerGoGoogle CloudHelmJavaKotlinKubernetesNumPyPandasPythonPyTorchRustscikit-learnTensorFlowXGBoost
Hume AI

About Hume AI

11-50 employees

Built from a decade of voice and emotion research. To build emotionally intelligent voice AI, we first had to define what “good” sounds like – not just acoustically, but perceptually and in real human conversations. That led us to build the research infrastructure behind expressive, trustworthy voice AI: scientifically grounded datasets, speech models, evaluation frameworks, and human preference pipelines. Today, we make that infrastructure available to frontier labs and AI-native companies building the next generation of voice. Whether you’re building foundation models, fine-tuning voice agents, or evaluating production systems, Hume provides the tools to measure, improve, and align voice AI the way people actually experience them.