AI Engineer - Model Performance
ABOUT FATHOM
We created Fathom to eliminate the needless overhead of meetings. Our AI assistant captures, summarizes, and organizes the key moments of your calls, so you and your team can stay fully present without sacrificing context or clarity. From instant, searchable call summaries to seamless CRM updates and team-wide sharing, Fathom transforms meetings from a source of friction into a place for alignment and momentum.
We’re a small company that creates magical experiences through the hard work of focused builders. We try to live our values - Care Deeply, Seek Leverage, Share Ownership, Sustain Urgency, and Be Tenacious - in everything we do, every day.
We started Fathom to rid us all of the tyranny of note-taking, and people seem to really love what we've built so far:
#1 Most Used App of the Year on HubSpot for 2025
#1 Rated on G2 with 4,500+ reviews and a perfect 5/5 rating
#1 Product of the Day and #2 AI Product of the Year
Most installed AI meeting assistant on both the Zoom and HubSpot marketplaces
We’re hitting revenue and usage records every week
We think you’ll be pretty excited about Fathom too if you give it a try. Sign up today (it’s free)!
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.
You'll own two things:
1. Inference performance. You'll make our models faster and cheaper — speculative decoding, quantization, serving configuration, GPU selection, batching strategies, cold start mitigation, adapter swapping. Our traffic is extremely spiky (meetings end in 30-minute blocks), so you need to think about throughput curves. Our team greatly values offering a fast product.
2. Fine-tuning pipelines. The AI team constantly fine-tunes models for new tasks — distilling large teacher models for classification, training adapters for domain-specific behavior, DPO for preference tuning. Right now each project reinvents the training loop. You'll build repeatable infrastructure so an AI Engineer can go more quickly from dataset to deployed model.
HOW YOU’LL HELP US WIN
• Benchmark FP8 quantization across GPU families, find that FP8 KV cache causes catastrophic repetition loops, identify static quantization as 6% faster than dynamic on certain hardware, and ship a production config that gets 1.3x speedup with Apply tot his job
Apply To this Job
We created Fathom to eliminate the needless overhead of meetings. Our AI assistant captures, summarizes, and organizes the key moments of your calls, so you and your team can stay fully present without sacrificing context or clarity. From instant, searchable call summaries to seamless CRM updates and team-wide sharing, Fathom transforms meetings from a source of friction into a place for alignment and momentum.
We’re a small company that creates magical experiences through the hard work of focused builders. We try to live our values - Care Deeply, Seek Leverage, Share Ownership, Sustain Urgency, and Be Tenacious - in everything we do, every day.
We started Fathom to rid us all of the tyranny of note-taking, and people seem to really love what we've built so far:
#1 Most Used App of the Year on HubSpot for 2025
#1 Rated on G2 with 4,500+ reviews and a perfect 5/5 rating
#1 Product of the Day and #2 AI Product of the Year
Most installed AI meeting assistant on both the Zoom and HubSpot marketplaces
We’re hitting revenue and usage records every week
We think you’ll be pretty excited about Fathom too if you give it a try. Sign up today (it’s free)!
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.
You'll own two things:
1. Inference performance. You'll make our models faster and cheaper — speculative decoding, quantization, serving configuration, GPU selection, batching strategies, cold start mitigation, adapter swapping. Our traffic is extremely spiky (meetings end in 30-minute blocks), so you need to think about throughput curves. Our team greatly values offering a fast product.
2. Fine-tuning pipelines. The AI team constantly fine-tunes models for new tasks — distilling large teacher models for classification, training adapters for domain-specific behavior, DPO for preference tuning. Right now each project reinvents the training loop. You'll build repeatable infrastructure so an AI Engineer can go more quickly from dataset to deployed model.
HOW YOU’LL HELP US WIN
• Benchmark FP8 quantization across GPU families, find that FP8 KV cache causes catastrophic repetition loops, identify static quantization as 6% faster than dynamic on certain hardware, and ship a production config that gets 1.3x speedup with Apply tot his job
Apply To this Job