AI Engineer - Model Performance

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ROLE OVERVIEW

We're hiring a Model Performance Engineer to own the speed, cost, and reliability of our model inference stack, and to build the fine-tuning infrastructure that makes the rest of the AI team faster.

This is not a research role. You'll be optimizing real systems serving millions of meetings — choosing between quantization trade-offs, debugging speculative decoding, or figuring out why one GPU family's tail latency explodes at high concurrency while another stays stable.

• Deep experience with LLM serving frameworks (vLLM, SGLang, TensorRT-LLM, or similar) — not just deploying them, but tuning them: attention backends, scheduling strategies, CUDA graph warmup, prefix caching

• Hands-on quantization experience — you've gone beyond "apply FP8 and hope." You understand weight vs activation quantization, per-channel vs per-tensor scaling, and when dynamic quantization introduces more overhead than it saves

• Production fine-tuning experience — LoRA/QLoRA SFT, familiarity with training frameworks (ms-swift, Axolotl, torchtune, or similar), understanding of data formatting, learning rate schedules, and how to diagnose training failures

• Strong Python. You'll write serving infrastructure, benchmarking harnesses, and training pipelines — not notebooks

• Comfort with GPU profiling and performance analysis. You should be able to look at a benchmark result and know whether the bottleneck is compute, memory bandwidth, or scheduling overhead

• Cost modeling for GPU infrastructure — you've had to choose between GPU types and justify the tradeoff

• Experience with multimodal models (audio/vision encoders + LLM decoders)

• Experience with Modal, Ray Serve, or similar serverless GPU platforms

• Understanding of audio processing (codecs, chunking, sample rates)

• Experience building internal tooling that other engineers use — this role succeeds when the rest of the team ships faster

• ML research background or publications

• Prompt engineering expertise (we have a team for that)

• Frontend or full-stack experience

• Masters/PhD (though it's fine if you have one)

• The opportunity to shape the foundational software services of a growing company

• A role that balances innovation and incremental improvement

• A dynamic and collaborative engineering team

• Competitive compensation and benefits

• A supportive environment that encourages innovation and personal growth

• Opportunity for impact. We’re established enough to ship instead of fighting fires and early enough that your work will have a real impact.

• Startup experience. You’ll work closely with our CEO, a 2X Founder/CEO with a background in computer science and product design.

• We embrace being fully remote. We schedule meetings sparingly and instead heavily use async comms (Slack, Notion, Loom)

• You’ll meet the entire team. We think it’s important that you get to meet everyone you’ll be working with.

• No bullshit. Ask us anything you like. We’ve never understood why companies pretend they’re something that they’re not in the hiring process - you’re going to find out eventually so we’d rather you know who we are up front so we can both make sure this is a good fit for all involved.

• Quick turnaround time. We know you have lots of options so we move fast usually in less than a week from start to finish.

Include a brief write-up or demo of inference optimization or model serving work you've done. We care about the reasoning behind your decisions — why you chose a specific quantization strategy, how you diagnosed a performance regression, what tradeoffs you navigated. A GitHub repo, blog post, or even a few paragraphs in your cover letter works.