Software Engineer, Artificial Intelligence​/LLM

We’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.

Build user-facing LLM features

• Design and implement retrieval-augmented generation and tool-calling flows using frameworks like Lang Chain 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 Type Script 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 Open Search, 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.

• 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.

• Experience with Bedrock, Open Search…