ML Systems Engineer: Scale Training & Inference

Doist; San Francisco (CA); On-site; USD 180,000 - 230,000; Full time

Benefits offered by this job

Competitive cash compensation

Startup equity

Job summary

Engram is seeking an ML Systems Engineer to design, optimize, and scale training and inference workloads across GPUs and distributed systems. You’ll collaborate with researchers and performance engineers to bridge cutting‑edge AI research with production systems.

The role requires a Bachelor’s degree and 5+ years of experience in ML or training/inference, contributing to personalization features, memory retrieval, and low‑latency serving in a fast‑paced startup environment based in San Francisco.

Qualifications

• 5+ years of experience with training or inference systems.
• Strong engineering fundamentals and ability to ship high‑quality code in a fast‑paced environment.
• Deep understanding of ML frameworks, GPUs, distributed systems, and infrastructure.
• Experience with open‑source ML or systems projects is a plus.

Responsibilities

• Design and optimize training and inference workloads for personalization and continual learning APIs.
• Collaborate with researchers to turn prototypes into scalable production systems.
• Improve serving paths for per‑user state and low latency requirements.
• Work on distributed training across GPUs and nodes.

Skills

Training/inference systems

ML frameworks PyTorch/JAX

GPU computing & distributed systems

Open-source ML experience

Education

Bachelor’s degree or equivalent experience in CS/Engineering

Tools

PyTorch

JAX

Job description

Engram is seeking an ML Systems Engineer to design, optimize, and scale training and inference workloads across GPUs and distributed systems. You’ll collaborate with researchers and performance engineers to bridge cutting‑edge AI research with production systems.

The role requires a Bachelor’s degree and 5+ years of experience in ML or training/inference, contributing to personalization features, memory retrieval, and low‑latency serving in a fast‑paced startup environment based in San Francisco.