# Machine Learning Engineer (Co-op / extended internship) at Engramme

> Onsite · United States · Intern · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Aengramme%3A36691537-e0fd-4aa9-9fcd-2618b433fac1 (read 2026-11-12).

## Facts (observed)

- Company: [Engramme](https://alderhq.app/companies/engramme)
- Location text: San Francisco
- Where it can be done from (inferred): Onsite · United States (evidence: "Location: San Francisco")
- Posted: 2026-10-15 (29 days ago)
- First seen by alder: 2026-11-07 · last verified: 2026-11-12
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/engramme/36691537-e0fd-4aa9-9fcd-2618b433fac1
- Skills listed (inferred): Python, LLMs, Distributed systems

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 55): p25 $110k, median $146k, p75 $153k

## Description (the employer's text)

This is a 6-month, in-person opportunity in San Francisco.

### About Engramme

Engramme is a Frontier AI lab spun out of Harvard focusing on AI x Human Memory. Persistent memory is a fundamental open problem at the heart of AI. Our vision is to build models of human memory, and to endow humans with perfect and infinite memory.

Before starting Engramme, the founding team has produced and commercialized extensive research in machine learning and neuro-science inspired memory models:

Gabriel Kreiman (CEO & Co-founder): Professor of Neuroscience and AI, Harvard Medical School. Led key research on brain memory consolidation at Harvard University.

Spandan Madan (CTO & Co-founder): PhD in Computer Science from Harvard, designed AI models which were commercially deployed at Adobe, Fujitsu, and Meta.

### What You’ll Do

Build automated eval pipelines for memory retrieval, compression, and reconstruction.

Design benchmarks for context retention, long-term consistency, and memory safety.

Analyze model behavior, surface failure modes, and define quality metrics.

Run large-scale experiments and maintain continuous evaluation systems.

Develop internal tools, dashboards, and workflows for fast debugging.

Integrate evals directly into production with backend + research teams.

### What We’re Looking For

Strong Python + ML framework experience.

Background in eval pipelines, model testing, or benchmarking.

Understanding of LLMs, embeddings, or retrieval systems.

Ability to analyze failures, define metrics, and reason from first principles.

Comfortable moving fast and owning core architecture.

### Nice to Have

Experience with vector DBs, similarity search, or interpretability.

Distributed systems or high-throughput pipeline experience.

Startup experience or ML tooling/open-source contributions.

Familiarity with monitoring/experiment-tracking tools.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aengramme%3A36691537-e0fd-4aa9-9fcd-2618b433fac1 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.