Member of Technical Staff — Model Optimization and Inference (New Grad)

About the Role

We can train a great model. The next problem is making it fast enough to actually use in a real-time conversation — and that gap is enormous. A model that responds in 3 seconds is a demo. A model that responds in under 500ms is a product.

We’re looking for someone who’s excited about taking trained models and squeezing every last millisecond out of them. You understand — or want to deeply understand — the full stack from model weights to serving infrastructure: quantization, KV cache optimization, kernel-level acceleration, batching strategies. You’ve worked with vLLM, SGLang, or similar frameworks (through coursework, research, internships, or open-source) and have opinions about where they fall short.

This posting is aimed at early-career engineers finishing or recently finished with a BS, MS, or PhD. We don’t require a PhD — we care about systems intuition, engineering chops, and the appetite to go deep.

Our stack is more complex than a standard LLM deployment: we’re serving a full-duplex multimodal system that must satisfy strict real-time latency constraints. There’s a lot of unsolved optimization work here, and we want someone who finds that genuinely exciting and is ready to grow fast alongside people who’ve built these systems before.

What You’ll Do

• Contribute to end-to-end inference optimization across our model stack — LLMs, audio models, and diffusion-based components
• Implement and tune KV cache strategies for long-context conversations, including eviction policies, compression, and memory-efficient attention
• Work with inference serving frameworks (vLLM, SGLang, TensorRT-LLM, etc.) and extend them for our specific workloads
• Profile and benchmark end-to-end latency and throughput; identify and systematically eliminate bottlenecks
• Build internal tooling that makes optimization work faster and more rigorous — profiling viewers, end-to-end inference test harnesses, and other infrastructure that helps the team move quickly
• Accelerate diffusion model inference — consistency models, step distillation, caching strategies, and custom kernel optimizations
• Apply quantization techniques (INT8, INT4, GPTQ, AWQ, and beyond) to reduce memory footprint and increase throughput without meaningfully degrading quality
• Work closely with research and infrastructure to ensure new models ship with optimized serving from day one

What We’re Looking For

• BS, MS, or PhD in CS, ML, or a related field — completed or in the final stretch
• Strong fundamentals in LLM inference or ML systems — KV caching, memory layout, attention kernels, batching, or serving — picked up through coursework, research, internships, or open-source. You don’t need to have shipped at production scale yet; you do need to learn fast and go deep.
• Exposure to inference serving frameworks (vLLM, SGLang, TensorRT-LLM, or similar) — even at a research or hobby level
• Strong Python and PyTorch skills; familiarity with CUDA or Triton is a significant plus
• A systematic approach to profiling and optimization — you measure first, then optimize
• Curiosity about diffusion inference, speculative decoding, quantization, or other inference-time acceleration techniques

Bonus Points

• Internship or research experience with LLM inference, ML systems, or model serving
• Contributions to open-source inference frameworks (vLLM, SGLang, TensorRT-LLM, etc.)
• CUDA / Triton kernel work, even at a research or hobby scale
• Publications or research projects in MLSys, model compression, or inference optimization
• Familiarity with multimodal or streaming inference architectures
• Experience with hard latency SLAs in any real-time system

Compensation

$200,000 – $300,000 base salary, plus meaningful equity. We think long-term ownership matters and structure equity accordingly.