{"id":4281476,"url":"https://alion.io/job/socure-staff-software-engineer","title":"Staff Software Engineer","company":{"id":2382,"name":"Socure","domain":"socure.com","url":"https://alion.io/company/socure","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":89,"open_postings":25,"ghost_share":0,"stale_share":0.24,"repost_share":0,"time_to_fill_p50_days":78,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Backend","role_family":"Backend","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States","New York, United States","Seattle, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":225000,"max":280000,"currency":"USD","period":"year","gross":null,"usd_annual":280000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Go","optional":false},{"name":"Java","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Search","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TypeScript","optional":false},{"name":"Amazon EKS","optional":true},{"name":"Amazon Neptune","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"ElasticSearch","optional":true},{"name":"GraphRAG","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Neo4j","optional":true},{"name":"OpenSearch","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-11-13T18:59:29Z","employer_posted_date":"2026-11-13","last_verified_at":"2026-11-14T02:12:48Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Why Socure?\nSocure 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.\nWe hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. 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.\n\nJob Overview\nWe are looking for a Software Engineer - Backend Platforms and Agentic & ML Systems to help build the backend, artificial intelligence, and machine-learning foundations that power Socure’s agentic investigation systems.\nThis is a hands-on engineering role for someone who wants to work across backend services, distributed systems, AI agents, retrieval, machine-learning models, and production infrastructure. You will design, implement, test, and operate software that supports agent orchestration, asynchronous workflows, knowledge retrieval, model integration, evaluation, and reliable production operation.\nYou will contribute to systems supporting multi-step investigations across use cases such as watchlist review, sanctions and PEP matching, adverse-media analysis, fraud-ring correlation, case management, and other risk and compliance workflows.\nA key part of the role is helping agents work with accurate, relevant, and timely domain knowledge. You will build capabilities that dynamically provide contextual information to agents, combine structured and unstructured data, support knowledge-graph and retrieval workflows, and preserve evidence and provenance for downstream decisions.\nYou will work with experienced engineers, machine-learning practitioners, product partners, and investigation experts to turn complex problems into dependable software. The role is calibrated for an engineer who can own well-scoped features and components, learn quickly, contribute to design discussions, and grow toward broader technical ownership.\nJob Responsibilities\nDesign, implement, test, and operate backend services that support agent orchestration, task execution, state management, model integrations, and investigation workflows.\n\nBuild synchronous and asynchronous workflows for agent tasks, including queues, retries, resumability, cancellation, idempotency, scheduling, and failure recovery.\n\nContribute to agent frameworks and developer harnesses that support tool calling, structured outputs, reusable skills, actor-based workflows, human review, and policy-controlled execution.\n\nImplement services that dynamically inject relevant knowledge into agent workflows based on investigation type, entity context, workflow state, customer policy, prior findings, and current task requirements.\n\nBuild domain-grounding capabilities that help agents distinguish authoritative facts, customer-defined policies, derived signals, hypotheses, and unverified observations.\n\nDevelop knowledge-ingestion and enrichment pipelines that normalize, classify, chunk, index, version, and refresh structured and unstructured data.\n\nImplement retrieval workflows using combinations of semantic search, lexical search, metadata filtering, hybrid retrieval, reranking, entity-aware retrieval, and temporal filtering.\n\nContribute to knowledge-graph systems that represent entities, relationships, events, evidence, provenance, and risk signals.\n\nSupport graph traversal, multi-hop retrieval, graph-enhanced context assembly, and integration between graph, vector, search, document, and API-based data sources.\n\nImplement context-selection logic that balances relevance, freshness, source authority, latency, tenant permissions, and model context limits.\n\nAdd safeguards against stale, contradictory, duplicated, irrelevant, poisoned, or unauthorized information entering agent contexts.\n\nBuild state and memory capabilities for agents, including short-term task state, investigation history, entity context, retrieval state, and provenance.\n\nIntegrate agents with internal APIs, identity and risk data sources, case-management systems, watchlist systems, model endpoints, webhooks, and event streams.\n\nDevelop secure connector and tool-execution patterns, including authentication, authorization, credential management, tenant isolation, audit logging, and sandboxed execution where appropriate.\n\nIntegrate machine-learning models used for classification, ranking, extraction, entity resolution, summarization, and other investigation-related capabilities.\n\nPartner with machine-learning and data-science teammates on feature definitions, training data preparation, labeling strategies, dataset quality, model interfaces, and deployment workflows.\n\nContribute to model training, fine-tuning, and adaptation workflows, including dataset preparation, experiment tracking, evaluation, versioning, deployment, and rollback.\n\nImplement model-serving and model-routing integrations that select models based on task requirements, latency, cost, quality, context, and data boundaries.\n\nImprove system performance through caching, batching, parallel execution, retrieval optimization, context reduction, model selection, prompt optimization, and efficient inference configuration.\n\nBuild evaluation and regression tests for retrieval quality, grounding, citation correctness, model behavior, tool selection, workflow completion, and end-to-end investigation outcomes.\n\nCreate test cases covering domain-specific scenarios, ambiguous cases, adversarial inputs, policy variations, knowledge freshness, retrieval failures, and production incidents.\n\nInstrument services with logs, metrics, traces, latency measurements, cost attribution, retrieval diagnostics, model-version tracking, and operational dashboards.\n\nUse production telemetry, failed runs, investigator feedback, and evaluation results to improve prompts, tools, memory, knowledge sources, model behavior, and workflow design.\n\nParticipate in on-call and incident response for systems owned by the team. Help improve monitoring, service-level objectives, capacity planning, and recovery procedures.\n\nWrite technical designs, document implementation decisions, participate in code and design reviews, and communicate trade-offs clearly.\n\nCollaborate with Product, Fraud and Risk, Investigation Operations, Platform Engineering, SRE, Security, Privacy, and Data Science.\n\nContribute to engineering standards for testing, observability, deployment, security, maintainability, and responsible AI development.\n\nJob Requirements\nRequired Qualifications\nBachelor’s or master’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.\n\nProfessional experience building software or backend services, including experience taking features from implementation through testing and production operation.\n\nExperience with backend development, API design, service-oriented architectures, asynchronous programming, testing, debugging, and performance optimization.\n\nUnderstanding of distributed-system concepts such as queues, retries, idempotency, timeouts, concurrency, partial failures, and service reliability.\n\nExperience with a programming language used for backend or data-intensive systems, such as Python, Java, Go, TypeScript, or a comparable language.\n\nExperience building applications that use large language models, AI agents, tool calling, retrieval-augmented generation, structured generation, or related generative-AI technologies.\n\nUnderstanding of agent architecture, including orchestration, state transitions, task execution, tool use, context management, memory, human escalation, and observability.\n\nExperience building backend services, agent components, developer tooling, data pipelines, or other reusable platform capabilities.\n\nExperience grounding AI or agent workflows with domain knowledge, entity context, customer policies, case history, live data, model outputs, or other task-relevant information.\n\nPractical understanding of retrieval concepts such as embeddings, vector search, lexical search, metadata filtering, hybrid retrieval, reranking, query expansion, or multi-stage retrieval.\n\nExperience with one or more of the following: knowledge graphs, graph databases, entity graphs, graph traversal, graph-enhanced retrieval, entity resolution, ontology or schema design, provenance, or temporal data modeling.\n\nExperience combining structured data, unstructured documents, search systems, vector retrieval, and live API-based data sources.\n\nUnderstanding of context assembly and prompt-context construction, including relevance, freshness, source authority, permissions, context size, latency, and deduplication.\n\nWorking knowledge of machine-learning concepts, including supervised learning, classification, ranking, embeddings, feature engineering, model evaluation, data quality, and model drift.\n\nExperience supporting machine-learning workflows such as dataset preparation, labeling, training, fine-tuning, experiment tracking, model evaluation, versioning, deployment, or monitoring.\n\nExperience evaluating AI or machine-learning systems using offline datasets, regression tests, production monitoring, task-level metrics, or qualitative review.\n\nExperience optimizing AI or ML systems for latency, throughput, cost, and quality through caching, batching, parallelism, retrieval optimization, context reduction, model selection, or inference configuration.\n\nExperience with cloud infrastructure and containerized environments, preferably AWS and Kubernetes.\n\nExperience with several of the following: relational databases, queues or event streams, object storage, caching systems, search platforms, vector databases, graph databases, and observability platforms.\n\nUnderstanding of service authentication, authorization, secrets management, tenant isolation, data protection, and privacy-sensitive system design.\n\nAbility to write maintainable, well-tested code and troubleshoot issues using logs, metrics, traces, debuggers, and other engineering tools.\n\nAbility to break down technical problems, estimate work, identify dependencies, raise risks, and deliver features with appropriate guidance.\n\nStrong written and verbal communication skills and an ability to collaborate effectively with engineering, product, data science, and operations partners.\n\nStartup DNA required: you thrive in fast-moving, high-ambiguity environments and take ownership like a founder, not an employee.\n\nPreferred Qualifications\nExperience building AI or machine-learning systems for fraud, identity, financial crime, compliance, trust and safety, investigations, or other regulated domains.\n\nExperience with agent frameworks, model gateways, evaluation and tracing platforms, GraphRAG, enterprise search, MCP-style connectors, or custom orchestration systems.\n\nExperience with AWS Bedrock, SageMaker, EKS, vLLM, GPU-backed inference, or comparable technologies.\n\nExperience deploying fine-tuned or domain-specific models alongside larger managed models.\n\nExperience with workflow engines, durable execution, distributed task processing, or long-running job orchestration.\n\nExperience with graph databases or graph-processing technologies such as Neo4j, Neptune, OpenSearch, Elasticsearch, or comparable systems.\n\nExperience building data-enrichment pipelines, entity-resolution systems, semantic search, knowledge platforms, or retrieval-augmented applications.\n\nExperience with model monitoring, feature and data drift detection, experiment tracking, model governance, or responsible AI practices.\n\nExperience improving agent and model behavior using traces, evaluation results, investigator feedback, retrieval diagnostics, and production failures.\n\nExperience working with fraud, identity, risk, compliance, or other 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