ML Systems Engineer: Personalization & Scalable Inference

Engram; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Startup equity

In‑person SF office

Competitive cash compensation

Job summary

Engram in San Francisco is hiring a ML Systems Engineer as a founding member of the engineering team. You will help design and scale the personalization and continual learning API that powers our models and agents, working closely with researchers and performance engineers.

You'll bridge research and platform engineering, shaping the deployment, testing, and production readiness of training and inference workloads.

Qualifications

• Bachelor’s degree in CS, engineering, or equivalent.
• 5+ years building training or inference systems.
• Experience shipping high-quality code in fast-paced environments.
• Strong understanding of ML frameworks and distributed systems.

Responsibilities

• Design and implement ML systems for personalization and continual learning APIs.
• Collaborate with researchers to turn prototypes into scalable production systems.
• Optimize serving paths for low latency and memory efficiency.
• Lead reliability, security, and engineering discipline as a founding hire.

Skills

5+ years experience

ML frameworks

Distributed systems

GPUs/ HPC

Ownership/initiative

Education

Bachelor’s degree in CS/Engineering

Tools

PyTorch

JAX

GPUs

Job description

Engram in San Francisco is hiring a ML Systems Engineer as a founding member of the engineering team. You will help design and scale the personalization and continual learning API that powers our models and agents, working closely with researchers and performance engineers.

You'll bridge research and platform engineering, shaping the deployment, testing, and production readiness of training and inference workloads.