Staff Software Engineer, Artificial Intelligence/LLM

We Love AI Jobs; San Carlos, Northern (CA, KY); Hybrid; USD 224,000 - 260,000; Full time

Job summary

Beacon AI in San Carlos hybrid environment is seeking a Staff Software Engineer to own LLM feature development for aviation safety applications. You will design retrieval and tool-calling flows, build robust APIs, and ensure low latency and scalable costs. You’ll lead safety guardrails and production monitoring across services and teams.

The role emphasizes building end-to-end LLM capabilities with strong emphasis on reliability, observability, and governance in a safety-critical domain.

Qualifications

• Proven track record building production-grade LLM features.
• Experience with retrieval Augmented Generation (RAG) and tool-calling.
• Familiarity with latency, cost, and reliability in safety-critical domains.

Responsibilities

• Build user-facing LLM features, including retrieval, tool-calling, validation, and fallback flows.
• Develop service APIs and workers, and tune caching, latency, and request costs.
• Create evaluations, production metrics, safety guardrails, and observability for deployed systems.

Skills

Python

TypeScript

LangChain

RAG and vector search

Tools

AWS Bedrock

OpenAI

Anthropic

OpenSearch

pgvector

Pinecone

Job description

# Staff Software Engineer, Artificial Intelligence/LLMBeacon AISan Carlos - HybridFull-time$224,000 – $260,000 / yearAI tools:LangChainAWS BedrockOpenAIAnthropicUpload your resume once and Scout applies for youApply with ScoutShare this job:LinkedInXSave imageYou’ll build and operate LLM-powered features for Beacon AI’s aviation safety platform, from retrieval and tool-calling services to evaluations and production monitoring. The role sets technical direction across teams and focuses on reliability, safety, and performance in a safety-critical domain.* Full-time* Hybrid* 8+ years### Skills and expertisePythonTypeScriptLangChainAWS BedrockOpenSearchpgvectorPineconeRAG and vector search### Key responsibilities* Build user-facing LLM features, including retrieval, tool-calling, validation, and fallback flows.* Develop service APIs and workers, and tune caching, latency, and request costs.* Create evaluations, production metrics, safety guardrails, and observability for deployed systems.## How this job comparesCompared with 94 open staff machine learning jobs on October 10, 2026.Pay: This job pays $224K to $260K a year. The 56 open staff machine learning jobs that publish US pay start at $219K on average, so this job starts in line with them.Similar open jobs: We Love AI Jobs lists 94 open staff machine learning jobs at 53 companies.Remote work: This job is hybrid. 16 of the 94 staff machine learning jobs (17%) are remote.AI in this role: High. AI is the core of the work: building, training or deploying AI systems. Building, evaluating, and operating LLM-powered product features is the core of the role. The posting specifically names retrieval-augmented generation, model integrations, tool-calling, and production evaluation.## Full descriptionAbout Beacon AIWe’re a fast-moving team of aviators, engineers, and operators building an AI platform to make flying safer, more efficient, and more capable. Backed by top investors, we’ve secured a dozen Department of Defense contracts and partnered with major airlines to deliver mission-critical systems. We operate without silos or heavy processes. Small, focused teams own what they build, ship quickly, and learn fast, pushing the boundaries of how humans and AI work together in aviation.You will ship LLM-powered product features end-to-end. That means designing retrieval and tool-calling flows, writing the services that run them, building evals and guardrails, and watching cost, latency, and quality in production. You’ll partner with the ML/infra teammates on embeddings, indexing, and model hosting, and with the product teammates on user experience and outcomes. We move fast, and we care about reliability in a safety-critical domain.This role is for engineers who set technical direction across services and teams (8+ years experience, with a track record of owning systems or defining standards others build against). You'll lead design for ambiguous, cross-team problems like provider routing or shared retrieval infra.What you’ll doBuild user-facing LLM features• Design and implement retrieval-augmented generation and tool-calling flows using frameworks like LangChain or equivalent primitives, where simpler is better.• Deliver robust JSON and schema-bound outputs with validation, retries, and fallbacks.• Add function calling to integrate with internal tools, search, routing, and data services.Own the service layer• Ship APIs and workers in Python or TypeScript with clear contracts, streaming, and backoff.• Add caching, request shaping, prompt templates, and context packing to control latency and cost.• Integrate with AWS Bedrock, OpenAI, Anthropic, or self-hosted endpoints as needed.Retrieval and data prep• Collaborate with infrastructure teammates to develop chunking, embeddings, and indexing capabilities for documents, time series, and multimedia.• Choose and tune vector backends such as OpenSearch, pgvector, or Pinecone.• Keep knowledge bases fresh with data syncs from S3, Aurora, DynamoDB, and external sources.Evaluation and quality• Create offline evals and golden sets for prompts, retrievers, and tools.• Stand up online metrics for task success, hallucination rate, retrieval precision/recall, p95 latency, and cost per request.• Run A/B tests and prompt/version rollouts with guardrails and canaries.Safety, privacy, and compliance• Implement content and policy checks, PII detection and redaction, access controls, and auditing.• Design human-in-the-loop paths for sensitive actions.• Handle aviation data with care and follow internal security standards.Operate what you build• Add tracing, logs, and dashboards for model calls, token usage, errors, and saturation.• Debug tricky failures across retrieval, prompts, tools, and providers.What will make you successful• Shipped LLM apps: You’ve put LLM features in front of users and improved them with data.• Strong builder: Comfortable writing production code, tests, and docs. You keep things simple and observable.• RAG and tools depth: You understand embeddings, chunking, vector search tradeoffs, and function calling.• Quality mindset: You design evals, define success metrics, and iterate based on evidence.• Cost and latency aware: You track p95, hit SLAs, and reduce cost without hurting quality.• Clear communicator: You explain tradeoffs and align partners across product, infra, and security.• Technical leadership: You've set direction or standards that other engineers or teams built against, not just shipped your own code.