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

• ▸Architecture research: beyond Transformers — dissect information flow across linear-attention / state-space / modern-RNN architectures (RWKV, Mamba / Mamba-2, RetNet, GDN gated delta net) and hybrids (e.g. our Qwen3.5 GDN + full-attention hybrid); map the functional roles of attention heads, MLPs, the residual stream, and recurrent / SSM hidden states.
• ▸Circuit interpretability (mechanistic): locate and reverse-engineer functional circuits inside models, attributing behavior to specific weights and features via activation patching, causal tracing, and sparse autoencoders (SAE).
• ▸Reasoning interpretability: study the internal representations behind multi-step reasoning / chain-of-thought; understand what triggers factual recall, tool calls, and refusals — grounding trustworthy on-device inference.
• ▸High-dimensional representation exploration: probe the geometry of features in high-dimensional activation / weight space (superposition, linearly separable directions, steering vectors) to enable personalization via hidden-state edits rather than prompt-stuffing.
• ▸Ship it: turn interpretability findings into the "what to change and why it's safe" evidence for Neural Imprint / self-learning, supporting fail-closed audits.

Ideal Experience

• ▸Strong foundations in deep learning and linear algebra / probability; able to read and reproduce frontier papers.
• ▸Hands-on mechanistic interpretability: activation patching, sparse autoencoders (SAE), logit lens, causal tracing.
• ▸Familiar with Transformer internals as well as linear-attention / state-space / RNN architectures (RWKV, Mamba, RetNet, GDN); understands how recurrent / SSM hidden states route information differently from attention.
• ▸High-dimensional data analysis and visualization (dimensionality reduction, probing, feature clustering).
• ▸Open-source contributions or published research in interpretability / representation learning.

We Hope You

• ▸A strong curiosity for opening the black box — you enjoy turning fuzzy phenomena into verifiable mechanisms.
• ▸Eager to translate research into shippable on-device capabilities, not stop at the paper.

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

• ▸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.