Make AI run on
every device

We Hope You

• ▸Have an engineering background
• ▸Deeply understand the edge AI landscape — cloud vs on-device trade-offs, privacy-first design, and user-centric AI
• ▸Love reading — stay current with AI research and industry trends
• ▸Have read at least 30 AI papers (100+ if born after 1998)
• ▸Can translate cutting-edge research into practical product roadmaps
• ▸Passionate about making AI accessible, private, and free for everyone

Tech Stack

Focus

• ▸Model compression: quantization (FP16→INT8→INT4), structured/unstructured pruning, and knowledge distillation.
• ▸Designing efficient model architectures optimized for edge deployment (1B–4B parameters).
• ▸Benchmarking inference performance across consumer hardware (Apple Silicon, Snapdragon, etc.).
• ▸Reproducing and improving state-of-the-art methods from deep learning literature.
• ▸Publishing research and contributing to open-source projects.

Ideal Experience

• ▸Deep understanding of Transformer architecture and large language models.
• ▸Hands-on experience with model compression techniques (quantization, pruning, distillation).
• ▸Familiarity with efficient architectures: MobileLLM, Phi, Gemma, and similar.
• ▸Performance profiling and optimization on resource-constrained devices.
• ▸Track record of open-source contributions or published research.

Tech Stack

Focus

• ▸Optimizing MLX framework internals for maximum performance on M-series chips.
• ▸Memory optimization: mmap acceleration, zero-copy model loading, unified memory utilization.
• ▸Integrating MLX with Apple frameworks (Core ML, Metal Performance Shaders).
• ▸Profiling and debugging ML workloads on Apple Silicon.
• ▸Building inference pipelines for on-device LLM, VLM, and TTS models.

Ideal Experience

• ▸Hands-on development with the MLX framework.
• ▸Low-level performance optimization on Apple hardware (M1–M5).
• ▸GPU programming with Metal or similar compute APIs.
• ▸Memory management and system-level optimization.
• ▸Contributing to open-source ML frameworks.

Tech Stack

Focus

• ▸Building native consumer apps: on-device translation, multimodal finance, food analysis, and more.
• ▸Integrating on-device ML models (LLM, VLM, TTS) into production iOS/macOS applications.
• ▸Optimizing app performance, memory footprint, and battery efficiency with AI workloads.
• ▸Designing delightful user experiences that leverage AI capabilities transparently.
• ▸Shipping and iterating products in the App Store.

Ideal Experience

• ▸Building and shipping polished iOS/macOS applications with Swift and SwiftUI.
• ▸Integrating Core ML or MLX models into native apps.
• ▸Performance optimization on mobile/desktop: memory, CPU, GPU usage.
• ▸Strong product sense — experience building user-facing consumer products.
• ▸Familiarity with on-device ML inference workflows.

Tech Stack

Focus

• ▸Building internal research tools, benchmarking dashboards, and model evaluation platforms.
• ▸Developing and maintaining AtomGradient's web presence and documentation sites.
• ▸Creating developer-facing tools for the open-source community.
• ▸End-to-end application development from backend services to frontend interfaces.

Ideal Experience

• ▸Building professional-grade web applications with React and TypeScript.
• ▸Backend service development in Rust or Python.
• ▸Experience with developer tools, documentation systems, or open-source project infrastructure.
• ▸Deploying and monitoring production web applications.
• ▸Interest in AI/ML and developer experience.