Memory Systems Engineer
2 weeks ago
About Particle Execution:
We're an AI solutions specialist and we're building modular AI infrastructure for execution - including a
second brain for our clients.
Second brain is a scalable knowledge and content-generation system that supports high-context AI agents for all our clients. It stores, learns from, and reasons with everything your business knows - enabling execution through strategy, content, and conversation.
We're now looking for an engineer who can work with the rest of our team and can help us turn per-client data into a persistent, intelligent memory system.
The Role:
We're hiring a Memory Systems Engineer to design and build the backbone of our product's long-term memory - initially for content automation, eventually for agentic workflows across industries. This is a hands-on role for someone who can ship fast, but also think deeply about system design. You'll work closely with the team to architect, test, and evolve how memory works across vector search, feedback integration, and contextual retrieval.
What You'll Build:
A modular, per-client
memory system
that supports:
Dense vector retrieval
(FAISS, Qdrant, Weaviate)
Feedback-aware updates
based on edits, ratings, and post performance
Dynamic context routing
based on generation type (e.g. strategy vs post)
A scalable infrastructure to support:
Dozens → thousands of clients
Low-latency retrieval and context injection for LLM generation
(Optional but welcome) Foundations for:
Graph-based or symbolic memory layers
Tree-of-thought traceability
Agent memory logs and replayable reasoning chains
You Might Be a Fit If You:
Have built or contributed to
LLM-integrated apps or systems Know how to build
RAG pipelines
from scratch (chunking, embedding, querying) Have worked with
vector databases
(FAISS, Qdrant, Pinecone, etc.) Have strong opinions on
how memory should evolve over time Care about building
real-time, adaptive, and traceable
AI systems Think pragmatically now, but architect for future generalization
Bonus Points For:
Experience with LangChain, LlamaIndex, or LangGraph Familiarity with graph databases (Neo4j, Dgraph) or key-value memory systems (Redis, DuckDB) Knowledge of hyperdimensional memory or symbolic-connectionist hybrids A portfolio or writeup of RAG or memory system design
What You'll Get:
Ownership over
core product infrastructure A chance to influence how
next-gen AI agents think and remember A team that moves fast, thinks big, and builds practically Competitive comp A growing customer base and a roadmap full of execution challenges
We're not just building AI that chats. We're building AI that remembers, improves, and executes. If you think memory is the key to agentic intelligence - we want to talk.
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