Senior ML Engineer

We are Amplifier, and we have built the world’s first Large Acoustic Model (LAM), a foundation model that uses human voice to detect health conditions. This is sci-fi becoming reality. We have raised significant capital from top-tier investors to turn this technology into a massive new category in healthcare.

We are looking for a heavy-hitter to join our engineering core. We don't need a manager; we need a high-level individual contributor who wants to spend 90% of their time building and shipping.

Let’s be clear about what we are signing up for.

We are entering a phase of hyper-growth. We are pushing ourselves—and this technology—further than most would consider reasonable. We are doing this because we believe the outcome (saving lives at scale) is worth the intensity required to get there.

• We work in person in San Francisco. We believe that the hardest problems are solved at a whiteboard, not over a Zoom call. We want the energy, the speed, and the camaraderie that comes from being in the arena together.
• We move fast. The feedback loop is immediate, and the standards are high. You will deploy code on Tuesday that is processing patient data on Wednesday.
• We have fun. We are a small, tight-knit crew on an adventure. We work hard because we love the game, not because we have to.

You will report to the Head of AI and act as the engine room for our model deployment. While the research team builds the models, you build the machine that makes them run.

Your primary focus is Scale, Reliability, and Latency of our Acoustic Model. You will own the serving infrastructure that allows us to process millions of voice biomarkers without breaking the bank (or the server).

The Challenge:

• Inference Optimization: Taking a massive transformer model and making it scream. You will work with TensorRT, ONNX, and quantization techniques to squeeze every ounce of performance out of our GPUs.
• Pipeline Architecture: Building the CI/CD pipelines for ML. You ensure that when Research commits a new model weights file, it seamlessly passes through testing and lands in production without downtime.
• Cluster Management: You will manage our Kubernetes clusters and GPU resources. You treat compute efficiency as a personal scorecard.

• A Systems Engineer First: You know that "ML Ops" is really just good distributed systems engineering. You are fluent in Kubernetes, Docker, and Terraform.
• Performance Obsessed: You know how to profile a model to find the bottleneck. You understand the difference between CPU and GPU bound tasks and how to optimize for both.
• Production Ready: You don't just write scripts; you write robust, testable, production-grade code (Python/Go/C++). You understand that "it works on my machine" is not a valid pull request.
• A Builder: You aren't looking to hire a team of 10 people to do the work. You want to be the one doing the work.

• Impact: The chance to build a product that literally saves lives.
• Equity: Meaningful ownership. As an early hire (top 15 employee), your equity package reflects the risk and potential of the stage we are at.
• The Team: You will work alongside a world-class team of researchers and founders. No bureaucracy. No politics. Just code.
• Resources: We are well-capitalized (oversized Seed), giving us the compute resources we need to execute.

Don't send a generic cover letter.

Send us your GitHub. Tell us about the most complex deployment pipeline you’ve built or a specific time you reduced inference latency by 50%.
[email protected]

Come build with us.