Digital Blinc Education Research and Technology

What You'll Own: You are responsible for making every model call in the platform as fast and cost-efficient as possible. You benchmark inference providers (Groq, Cerebras, Fireworks), configure speculative decoding, and ensure the Model Router always picks the fastest available free-tier endpoint. Responsibilities: Benchmark Groq, Cerebras, Together AI, Fireworks, and OpenRouter across latency, throughput, and accuracy on agentic tasks Configure Ollama 0.3.x with speculative decoding for local fallback models Build and maintain the ModelRouter class with automatic rate-limit detection and provider rotation Profile memory and token usage per worker agent and reduce average cost per task by 40% Write inference optimization documentation for the team Requirements: Python (intermediate) — can read and write async code Familiarity with REST APIs and JSON Basic understanding of LLMs (knows what tokens, temperature, and context window mean) Bonus: Prior exposure to Ollama, LM Studio, or any local model runner You'll Learn: vLLM, Cerebras WSE-3 API, OpenRouter free routing, speculative decoding, provider failover architecture