Applied AI Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Childcare support

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Work mode

Onsite From the posting source checked Jun 20, 2026

In-office days

5 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Applied AI Engineer San Francisco Bay Area | OnSite Role Summary: Own the 0→1. You'll turn vague customer use cases into working proofs-of-concept that showcase what Mem0 can do. This means rapid full-stack prototyping, stitching together AI tools, and aggressively experimenting with memory retrieval approaches until the use case works end-to-end. You'll partner closely with Research and Backend, communicate trade-offs clearly, and hand off winning prototypes that can be hardened for production. What You'll Do: - Build POCs for real use cases: Stand up end-to-end demos (UI + APIs + data) that integrate Mem0 in the customer's flow. - Experiment with memory retrieval: Try different embeddings, indexing, hybrid search, re-ranking, chunking/windowing, prompts, and caching to hit task-level quality and latency targets. - Prototype with Research: Implement paper ideas and new techniques from scratch, compare baselines, and keep what wins. - Create eval harnesses: Define small gold sets and lightweight metrics to judge POC success; instrument demos with basic telemetry. - Integrate AI tooling: Combine LLMs, vector DBs, Mem0 SDKs/APIs, and third-party services into coherent workflows. - Collaborate tightly: Work with Backend on clean contracts and data models; with Research on hypotheses; share learnings and next steps. - Package & handoff: Write concise docs, scripts, and templates so Engineering can productionize quickly. Minimum Qualifications - Full-stack fluency: Next.js/React on the front end and Python backends (FastAPI/Django/Flask) or Node where needed. - Strong Python and TypeScript/JavaScript; comfortable building APIs, wiring data models, and deploying quick demos. - Hands-on with the LLM/RAG stack:

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