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Senior Software Engineering Manager – AI Platform
| Company | Humane |
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| Location | San Francisco, CA, USA |
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| Salary | $220000 – $270000 |
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| Type | Full-Time |
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| Degrees | |
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| Experience Level | Expert or higher |
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Requirements
- 10+ years of hands-on software development experience, including recent expertise in distributed systems, networked devices, or embedded AI.
- 3+ years of engineering management experience.
- Strong background in machine learning model deployment, including LLMs, computer vision, or speech models.
- Experience integrating AI models on edge devices, IoT platforms, or hybrid cloud-edge systems.
- Ability to quickly adapt to new AI advancements and apply them to scalable real-world applications.
- Fast-paced, iterative approach to AI development and deployment.
- 5+ years of experience with Java or C++.
Responsibilities
- Develop and optimize edge inference pipelines to run AI models efficiently on resource-constrained devices while integrating with cloud services.
- Design and implement hybrid cloud-edge AI architectures that balance local inference with cloud scalability.
- Integrate and third-party machine learning APIs, while also training and optimizing open source models for real-time AI applications.
- Build distributed systems and networked AI devices, ensuring seamless connectivity and interoperability between edge and cloud environments.
- Evaluate on-device AI accelerators and emerging hardware solutions to maximize performance, security, and efficiency.
- Drive new features from prototype to production, collaborating closely with AI researchers, infrastructure engineers, and designers.
- Work on privacy-first AI architectures, ensuring data security and compliance in localized AI deployments.
- Develop robust testing and validation pipelines for non-deterministic AI systems, including LLMs and real-time inference models.
Preferred Qualifications
- Expertise in on-device ML optimization techniques (e.g., quantization, pruning, distillation).
- Strong understanding of low-latency computing, edge inference, and privacy-preserving AI.
- Familiarity with AI hardware accelerators (e.g., NPUs, TPUs, GPU-based inference).
- Experience evaluating and integrating third-party inference engines (e.g., TensorFlow Lite, ONNX Runtime, CoreML).
- Knowledge of cloud-edge hybrid architectures, including distributed AI model serving and orchestration.
- Experience developing or working with open-source ML frameworks.
- Prompt engineering and experimentation with AI-powered hardware.
