Machine Learning Engineer
Description For Machine Learning Engineer
Beam is an ultrafast AI inference platform. We built a serverless runtime that launches GPU-backed containers in less than 1 second and quickly scales out to thousands of GPUs. Developers use our platform to serve apps to millions of users around the globe. We're backed by Y Combinator, Tiger Global, and prominent developer-tool founders, including the founder of Snyk and former CTO of GitHub.
Our team works in-person in New York City, but we welcome remote applicants who are exceptionally qualified.
We're a small, highly-technical team, with backgrounds in distributed systems and robotics. We've raised $7M from YC, Tiger, Guy Podjarny (Founder of Snyk), and Jason Warner (former CTO of Github).
Our mission is to build the world's best compute platform for AI. Our first product is a serverless inference platform, used by companies like Coca Cola, Geospy and hundreds more. We've built our own container runtime, called beta9, which is designed for launching GPU-backed containers in under 1s.
In this role, you'll optimize inference performance for a wide range of models running on our platform. You will minimize latency, maximize throughput, and continuously experiment to achieve industry-leading performance. Your work will directly impact millions of users worldwide.
We're searching for intensely curious, passionate, and hard-working engineers to join our mission in rebuilding the cloud for the age of AI. The ideal candidate will have:
- Experience with state-of-the-art inference stack (PyTorch, TensorRT, vLLM)
- Familiarity with modern AI workflows, like ComfyUI and LoRA adaptors
- Deep understanding of model compilation, quantization, and serving architectures
- Experience with GPU architectures and kernel-level optimizations
- Proficiency in CUDA, Triton, or similar low-level accelerator frameworks
Responsibilities For Machine Learning Engineer
- Optimize inference performance for various models
- Minimize latency and maximize throughput
- Continuously experiment to achieve industry-leading performance
- Work on GPU-backed container infrastructure
- Impact millions of users worldwide
Requirements For Machine Learning Engineer
- Experience with the state-of-the-art inference stack (e.g., PyTorch, TensorRT, vLLM)
- Familiar with modern AI workflows, like ComfyUI and LoRA adaptors for fine-tuning
- Deep understanding of model compilation, quantization, and serving architectures
- Familiarity with GPU architectures and comfort in diving into kernel-level optimizations
- Experience programming with CUDA, Triton, or similar low-level accelerator frameworks
Benefits For Machine Learning Engineer
- Work on challenging and impactful engineering problems
- Competitive salary and meaningful equity
- Join a fast-growing pre-Series A company at the ground floor
- Health, dental, and vision benefits with 90% coverage for employees and 50% for dependents
- Opportunities to participate in events across the cloud-native and AI communities
- Fitness stipend, learning budget
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