Staff Software Engineer
Socure · Hub - San Francisco
About this role
## Why Socure Socure is building the identity trust infrastructure for the digital economy—verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself, keep reading. --- ## Job Overview We’re looking for a **Software Engineer – Backend Platforms and Agentic & ML Systems** to help build the backend, AI, and machine-learning foundations that power Socure’s agentic investigation systems. This is a hands-on role spanning **backend services, distributed systems, AI agents, retrieval, ML models, and production infrastructure**. You’ll design, implement, test, and operate software for **agent orchestration, asynchronous workflows, knowledge retrieval, model integration, evaluation, and reliable production operation**. You’ll contribute to systems supporting multi-step investigations across use cases such as: - Watchlist review - Sanctions & PEP matching - Adverse-media analysis - Fraud-ring correlation - Case management - Other risk and compliance workflows A key part of the role is helping agents work with **accurate, relevant, and timely domain knowledge**, including: - Dynamically providing contextual information to agents - Combining structured + unstructured data - Supporting knowledge-graph and retrieval workflows - Preserving evidence and provenance for downstream decisions --- ## Job Responsibilities - Design, implement, test, and operate backend services for agent orchestration, task execution, state management, model integrations, and investigation workflows. - Build synchronous and asynchronous workflows (queues, retries, resumability, cancellation, idempotency, scheduling, failure recovery). - Contribute to agent frameworks and developer harnesses (tool calling, structured outputs, reusable skills, actor-based workflows, human review, policy-controlled execution). - Implement services that inject relevant knowledge into agent workflows based on investigation type, entity context, workflow state, customer policy, prior findings, and task requirements. - Build domain-grounding capabilities to distinguish authoritative facts, policies, derived signals, hypotheses, and unverified observations. - Develop knowledge-ingestion and enrichment pipelines (normalize, classify, chunk, index, version, refresh). - Implement retrieval workflows (semantic + lexical search, metadata filtering, hybrid retrieval, reranking, entity-aware retrieval, temporal filtering). - Contribute to knowledge-graph systems (entities, relationships, events, evidence, provenance, risk signals). - Support graph traversal, multi-hop retrieval, graph-enhanced context assembly, and integration across graph/vector/search/document/API sources. - Implement context-selection logic balancing relevance, freshness, source authority, latency, tenant permissions, and model context limits. - Add safeguards against stale/contradictory/duplicated/irrelevant/poisoned/unauthorized information entering agent contexts. - Build agent state and memory (task state, investigation history, entity context, retrieval state, provenance). - Integrate agents with internal APIs and systems (identity/risk data sources, case management, watchlists, model endpoints, webhooks, event
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