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Beacon Venture Capital
Beacon Venture Capital
1 – 10 Angajați
FintechInteligență artificială
Beacon Venture Capital este divizia de capital de risc corporativ deținută integral de Kasikornbank PLC, una dintre principalele bănci comerciale din Thailanda. Fondul a fost înființat în 2016 și investește strategic în startupuri din domeniul tehnologiei, de la etapele incipiente până la etapele de creștere, cu un accent principal pe fintech. Printre domeniile sale de interes investițional se numără și afacerile din zona stilului de viață al consumatorilor, precum și domenii de tehnologie avansată, cum ar fi inteligența artificială și tehnologia informației pentru companii. Beacon Venture Capital a fost lansat cu un capital de 30 de milioane de dolari, care a crescut la 135 de milioane de dolari până în 2018.
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Staff Software Engineer, Artificial Intelligence and LLM

Lead development of LLM-powered aviation capabilities for Beacon AI’s safer-flight platform, covering RAG, tool calling, evaluation, safety, and production reliability.

USStatele Unite ale AmericiiCACalifornia
224.000 $ – 260.000 $ / an
HibridNormă întreagăLiderLimbă Obligatorie: englezăInginer AIInginer de învățare automată
AWSDynamoDBPythonTypeScript

Descriere

  • Build user-facing LLM product capabilities from design through production
  • Architect retrieval-augmented generation and tool-calling workflows
  • Produce reliable JSON and schema-constrained outputs with validation, retries, and fallback handling
  • Use function calling to connect internal tools, search, routing, and data services
  • Develop Python or TypeScript APIs and workers with explicit contracts, streaming, and backoff
  • Use caching, request shaping, prompt templates, and context packing to manage latency and cost
  • Connect applications to AWS Bedrock, OpenAI, Anthropic, or self-hosted model endpoints
  • Work with infrastructure engineers on chunking, embeddings, and indexing for documents, time series, and multimedia
  • Select and optimize vector databases including OpenSearch, pgvector, and Pinecone
  • Keep knowledge bases current by synchronizing data from S3, Aurora, DynamoDB, and external sources
  • Build offline evaluations and golden datasets for prompts, retrievers, and tools
  • Define online measures for task success, hallucination rate, retrieval precision and recall, p95 latency, and per-request cost
  • Conduct A/B tests and roll out prompt and version changes with guardrails and canary releases
  • Implement content and policy enforcement, PII detection and redaction, access controls, and audit trails
  • Create human-in-the-loop workflows for sensitive operations
  • Process aviation data in line with internal security standards
  • Provide tracing, logging, and dashboards for model calls, token usage, errors, and system saturation
  • Investigate failures across retrieval, prompts, tools, and model providers
  • Set technical direction across services and teams for complex, ambiguous problems
  • Work with ML, infrastructure, and product partners on user experience, outcomes, reliability, and safety-critical systems

Cerințe

  • At least 8 years of experience, including ownership of systems or standards used by other engineers
  • Alternatively, a related master’s degree plus 4 years of relevant experience
  • Experience launching LLM features for end users and improving them through data
  • Strong production development, testing, and technical documentation skills
  • Knowledge of embeddings, chunking, vector-search tradeoffs, and function calling
  • Experience designing evaluations, establishing success metrics, and iterating from evidence
  • Ability to monitor p95 latency, satisfy SLAs, and lower costs while preserving quality
  • Ability to communicate tradeoffs and align product, infrastructure, and security stakeholders
  • Technical leadership experience setting direction or standards adopted by engineers or teams
  • Proficiency in Python or TypeScript
  • Experience with RAG, tool-calling workflows, schema-constrained JSON, validation, retries, fallbacks, APIs, workers, streaming, backoff, caching, prompt templates, context packing, and model providers
  • Experience using AWS Bedrock, OpenAI, Anthropic, or self-hosted endpoints
  • Familiarity with OpenSearch, pgvector, Pinecone, or Weaviate
  • Experience working with S3, Aurora, and DynamoDB data sources
  • Experience with offline evaluation, golden datasets, online metrics, A/B testing, prompt and version rollouts, guardrails, and canary releases
  • Experience implementing content and policy controls, PII detection and redaction, access management, and auditing
  • Experience with tracing, logging, dashboards, model-call troubleshooting, and production operations
  • U.S. Person status is required; visa sponsorship and transfers are not available
  • All work must be performed within the United States
  • Ability to work in the San Francisco Bay Area or San Carlos, California, at least 3 days per week onsite

Beneficii

  • Equity is offered
  • The company covers 100% of employee medical premiums
  • The company covers 25% of dependent medical premiums
  • Three weeks of paid time off
  • At least 13 paid company holidays
  • 401(k) plan offered
  • Remote work is available on the remaining hybrid-schedule days

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