AI Engineer

• Design and implement retrieval-augmented generation and tool-calling flows
• Deliver robust JSON and schema-bound outputs with validation, retries, and fallbacks
• Integrate function calling with internal tools, search, routing, and data services
• 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
• Collaborate on chunking, embeddings, and indexing for documents, time series, and multimedia
• Choose and tune vector backends such as OpenSearch, pgvector, or Pinecone
• Maintain knowledge bases through data synchronization from S3, Aurora, DynamoDB, and external sources
• Create offline evaluations and golden sets for prompts, retrievers, and tools
• Establish 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
• Implement content and policy checks, PII detection and redaction, access controls, and auditing
• Design human-in-the-loop paths for sensitive actions
• Add tracing, logs, and dashboards for model calls, token usage, errors, and saturation
• Debug failures across retrieval, prompts, tools, and providers
• Partner with product, infrastructure, ML, and security teammates to deliver reliable aviation systems
• Build user-facing LLM features for Beacon AI’s platform that makes flying safer, more efficient, and more capable

Senior Software Engineer – Artificial Intelligence/LLM

• Build user-facing LLM-powered product features end-to-end
• Design and implement retrieval-augmented generation and tool-calling flows
• Deliver robust JSON and schema-bound outputs with validation, retries, and fallbacks
• Add function calling to integrate internal tools, search, routing, and data services
• 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
• Collaborate with infrastructure teammates on chunking, embeddings, and indexing for documents, time series, and multimedia
• Choose and tune vector backends such as OpenSearch, pgvector, or Pinecone
• Maintain fresh knowledge bases through data synchronization from S3, Aurora, DynamoDB, and external sources
• Create offline evaluations and golden sets for prompts, retrievers, and tools
• Establish 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
• 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 according to internal security standards
• Add tracing, logs, and dashboards for model calls, token usage, errors, and saturation
• Debug failures across retrieval, prompts, tools, and providers
• Set technical direction across services and teams for ambiguous, cross-team problems
• Partner with ML/ infrastructure and product teammates on user experience, outcomes, reliability, and safety-critical systems

Staff Software Engineer, Artificial Intelligence/LLM

• Develop scalable infrastructure and tools supporting model training, model deployment to edge devices, egression, rules-based models, operations, and inference
• Develop and optimize the AI training platform
• Improve data processing pipelines
• Accelerate model training and inference workflows
• Enhance testing infrastructure
• Support tool development, instrumentation, and system monitoring to boost reliability, reduce latency, and increase iteration speed
• Suggest, collect, and synthesize requirements with managers or tech leads to create feature roadmaps
• Code deliverables in tandem with the engineering team
• Adapt standard machine learning methods to exploit distributed clusters, multicore SMP, and GPU environments
• Perform team-specific responsibilities

Summer Intern – Machine Learning Engineer, AV/AI Platform

• Develop LLM/NLU systems to process and extract meaningful information from clinical notes and medical documents
• Classify patients according to level-of-care guidelines and make accurate recommendations
• Establish best practices for LLM/AI systems, benchmarking/evaluation frameworks, and model governance
• Build scalable data pipelines while maintaining strict data privacy and security standards
• Collaborate with backend engineers to integrate AI/ML capabilities into the Onos platform
• Build and operationalize AI/data pipelines for analyzing medical records and streamlining clinical assessments and healthcare quality reviews
• Benchmark LLM systems to extract evidence from medical records and classify patients’ level-of-care recommendations
• Develop and optimize systems that ingest medical standards-of-care documents and evaluate provider adherence to guidelines
• Design explainable AI solutions that provide transparency into model decisions for healthcare professionals
• Take ownership of a significant part of the Onos platform while working as an early team member across backend and data engineering responsibilities

Staff Software Engineer, AI

• Build systems that determine what message a member receives, when they receive it, and which channel is most helpful
• Own the production path that turns models and AI capabilities into dependable member-facing experiences
• Design and operate deployment, serving, release automation, monitoring, rollback, orchestration, and backend integration systems
• Build context on the member experience, communication systems, model lifecycle, data flows, and operational requirements
• Establish baselines for AI-backed service health, including service-level objectives, deployment paths, observability, failure modes, and operational ownership
• Ship improvements to developer experience, testing, release safety, and incident response
• Own production MLOps or distributed-systems initiatives from technical planning through rollout, measurement, and iteration
• Build or improve safe promotion patterns including versioning, configuration, feature flags, canaries, rollback, and recovery
• Define durable interfaces, data contracts, feature inputs, freshness expectations, evaluation hooks, and production-readiness criteria
• Operate online and batch inference capabilities meeting contracts for latency, availability, correctness, resiliency, observability, and cost
• Create reusable patterns for APIs, services, queues, durable workflows, monitoring, and incident response
• Integrate AI into notifications, personalization, send-time optimization, and related experiences
• Shape technical decisions, mentor engineers, conduct design and code reviews, and improve engineering workflows
• Connect system reliability to message relevance, engagement, reduced communication fatigue, and sustained participation in care

Senior AI Engineer, MLOps, Distributed Systems

• Build and operate backend systems supporting AI-powered product capabilities
• Design APIs, services, workflows, and data flows for reliable production operation
• Integrate, deploy, and operate ML models and AI services within product systems
• Learn the Proactive Communications & Notifications domain, service architecture, operational practices, and member-impact goals
• Ship improvements to APIs, services, asynchronous jobs, and workflows
• Participate in testing, observability, deployment, on-call, and incident-response practices
• Own medium-sized features from planning through implementation, testing, rollout, and follow-up
• Integrate ML- or AI-backed capabilities into product workflows with reliable data flows and failure handling
• Improve distributed-system correctness and operability using retries, idempotency, versioning, graceful degradation, dashboards, and structured logging
• Deliver production initiatives improving the relevance, timeliness, reliability, or maintainability of member communications
• Trace and resolve issues across APIs, services, queues, databases, model-backed components, and data dependencies
• Contribute through code reviews, design documentation, pairing, onboarding support, and engineering practices
• Collaborate with senior engineers, ML Scientists, Data Engineering, Product, and software engineers

AI Engineer II

• Build and improve systems that extract and structure information from large volumes of unstructured documents.
• Develop reliable backend services and systems that support document processing and AI-powered products.
• Work with engineers and data scientists to integrate AI technologies into production systems.
• Improve the performance, reliability, and scalability of systems.
• Build tools and infrastructure that help teams develop, evaluate, and operate AI-powered systems.
• Participate in the full project lifecycle, from design and implementation to testing, deployment, and iteration.
• Collaborate with product, engineering, data science, and machine learning teams to solve complex problems.
• Learn new technologies and contribute to the evolution of the AI platform.

Software Engineer, AI Entities

• Design, build, and ship complex and critical platform components, including backend services, APIs, and data pipelines
• Build AI/ML-powered product features end to end, integrating models and designing surrounding systems and data flows
• Own technical architecture for major systems, making hands-on decisions for scalability, performance, security, and cost, then implementing them
• Lead engineering best practices across the full SDLC, including code quality, testing, security, observability, and operational excellence
• Build resilient, well-tested services that integrate across products and with external healthcare systems
• Ensure software complies with healthcare and data-protection requirements, including HIPAA and GDPR
• Debug, profile, and harden production systems, owning reliability and incident response
• Provide hands-on technical mentorship through design reviews, pairing, and code review
• Partner with product and clinical stakeholders to turn real-world needs into technically sound designs
• Evaluate and select technologies, frameworks, and tooling while balancing capability, cost, risk, and time-to-value

Staff Artificial Intelligence Engineer

• Make frontier AI data generation and evaluation faster, cheaper, and more rigorous with ML and AI
• Study how frontier-grade data is generated and evaluated
• Form hypotheses and validate them against real production data
• Ship successful approaches at scale
• Cut the cost of long-horizon agent evaluation using adaptive sampling, statistically grounded early stopping, model cascades, caching, and cheap-first gating
• Route evaluation and judge calls to the cheapest model that clears the quality bar, with fallback, monitoring, and cost attribution
• Fine-tune and serve open-weight models using LoRA and other parameter-efficient methods
• Build models that estimate task difficulty for frontier systems before rollout
• Build golden datasets and quantify LLM-as-judge accuracy and calibration
• Turn research prototypes into reusable, configurable components for deployed engineers and researchers
• Help shape ML engineering standards, discipline direction, and the growing team

Senior Software Engineer – AI / ML

• Design, build, and ship LLM-powered features end-to-end, including prompt design, retrieval, tool use, agents, evaluation, and production operations
• Build evaluation harnesses and feedback loops for responsible AI feature delivery
• Integrate AI capabilities into patient, clinician, and operational workflows
• Partner with clinical, product, and safety stakeholders to define quality standards
• Contribute to internal AI-for-engineering practices and tooling
• Stay current with AI research and the ecosystem and bring relevant advances to the team

AI Engineer jobs in California