Machine Learning Engineer (Co-op / extended internship)
San Francisco, CAInternshipPosted todayStill listed today
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At a glance
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Job overview
Engramme is a frontier AI lab focused on AI and human memory. The Machine Learning Engineer co-op will build evaluation pipelines and benchmarks for memory retrieval, compression, reconstruction, context retention, consistency, and safety. The role also involves analyzing model behavior, running experiments, developing debugging tools, and integrating evaluations with backend and research teams. The opportunity is in person in San Francisco and lasts six months.
Skills & qualifications
Skills
Full job description
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:
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Gabriel Kreiman (CEO & Co-founder): Professor of Neuroscience and AI, Harvard Medical School. Led key research on brain memory consolidation at Harvard University.
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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
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Build automated eval pipelines for memory retrieval, compression, and reconstruction.
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Design benchmarks for context retention, long-term consistency, and memory safety.
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Analyze model behavior, surface failure modes, and define quality metrics.
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Run large-scale experiments and maintain continuous evaluation systems.
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Develop internal tools, dashboards, and workflows for fast debugging.
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Integrate evals directly into production with backend + research teams.
What We’re Looking For
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Strong Python + ML framework experience.
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Background in eval pipelines, model testing, or benchmarking.
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Understanding of LLMs, embeddings, or retrieval systems.
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Ability to analyze failures, define metrics, and reason from first principles.
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Comfortable moving fast and owning core architecture.
Nice to Have
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Experience with vector DBs, similarity search, or interpretability.
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Distributed systems or high-throughput pipeline experience.
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Startup experience or ML tooling/open-source contributions.
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Familiarity with monitoring/experiment-tracking tools.
How this job compares
Set against the 6,204 open Machine Learning Engineer jobs on Olive Jobs, counted November 7, 2026.
- Pay
- This postingNot listed
- California median$179–263K/yr1,442 salary ranges
- California median$53–70/hr34 hourly ranges
- Openings
- Olive Jobs lists 1,918 open Machine Learning Engineer jobs in California and 6,204 across the US. 8% of them nationwide are listed as remote or flexible.
- Experience
- Machine Learning Engineer postings that state a minimum ask for 5 years of experience at the median; the middle half ask for 3 to 7 years (4,888 postings).
- Education
- 2,154 of the 6,204 open Machine Learning Engineer jobs (35%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 7, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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