Research Engineer - Memory & Contextual AI Prototypes

ENGRAM Lab; San Francisco (CA); On-site; USD 130,000 - 190,000; Full time

Job summary

ENGRAM Lab in San Francisco seeks a Research Engineer to bridge cutting-edge ML research with production systems. Translate papers into prototypes, collaborate with researchers, and build experimental systems for memory retrieval and contextual understanding.

You will optimize code for deployment, design experiments, and contribute to publications and documentation while communicating across research and engineering teams to advance practical AI capabilities.

Qualifications

• 3+ years of experience in research engineering or applied ML; ability to translate papers into prototypes.
• Strong programming skills in Python and ML frameworks; familiarity with PyTorch or TensorFlow.
• Experience implementing research papers and prototyping novel ideas.
• Knowledge of ML fundamentals and deep learning concepts.
• Ability to work independently and manage research projects.
• Strong problem-solving and analytical skills.
• Experience with experimentation frameworks and A/B testing.
• Good communication skills for cross-functional collaboration.

Responsibilities

• Translate research papers and ideas into working prototypes.
• Collaborate with research scientists to implement novel algorithms.
• Build experimental systems for memory retrieval and contextual understanding.
• Optimize research code for production deployment.
• Design and run experiments to validate research hypotheses.
• Contribute to research publications and technical documentation.
• Bridge communication between research and engineering teams.

Skills

Python

Machine Learning

Analytical skills

Communication

Independent work

Tools

PyTorch

TensorFlow

Job description

ENGRAM Lab in San Francisco seeks a Research Engineer to bridge cutting-edge ML research with production systems. Translate papers into prototypes, collaborate with researchers, and build experimental systems for memory retrieval and contextual understanding.

You will optimize code for deployment, design experiments, and contribute to publications and documentation while communicating across research and engineering teams to advance practical AI capabilities.