Company Profile
FeaturedGroq
Groq builds AI inference hardware and cloud inference services optimized for low-latency, high-throughput model serving.
What They Build
AI Inference Hardware and Inference Cloud
Customer Type
AI developers, model providers, and enterprise inference workloads
Business Model
Infrastructure usage and enterprise platform contracts
Key Products & Initiatives
- Inference performance is the central product differentiator.
- Hardware-software co-design drives latency and throughput outcomes.
- Ecosystem fit depends on practical model compatibility and developer tooling.
Key Products & Brands
LPU Architecture
AI HardwareInference-focused chip architecture designed for predictable speed.
GroqCloud
Inference ServiceHosted inference service for running LLM and AI workloads.
Model Ecosystem Integrations
Developer EnablementCompatibility and tooling for practical deployment workflows.
Role Families
Compiler & Runtime Systems
Expected Skills
What They Work On
- Developing the Groq Compiler to deterministically schedule instructions.
- Optimizing PyTorch/ONNX model conversion for the LPU.
- Building the low-level runtime and driver stack.
Portfolio Ideas
- Building a toy compiler backend for a custom ISA.
- Creating a matrix multiplication kernel in assembly.
- Designing a graph optimization pass for a neural network.
LPU Hardware Architecture
Expected Skills
What They Work On
- Designing the next-generation Tensor Streaming Processor (TSP) architecture.
- Verifying chip logic and memory systems.
- Optimizing power and thermal performance for efficiency.
Portfolio Ideas
- Building a Verilog AXI bus interface.
- Creating a cache coherence simulator.
- Designing a pipelined CPU core.
Entry Pathways
internships
Internships exist across hardware and platform functions.
entry Level Roles
Entry roles available in selected hardware, software, and operations teams.
graduate Programs
Role-based specialized hiring.
Culture Signals
Performance engineering is a core cultural value.
Technical rigor and benchmarking discipline are strongly emphasized.
Execution focuses on practical deployment, not research hype alone.