Beacon Venture Capital

Recruitment

• Coordinate interviews across time zones and ensure a smooth candidate experience
• Manage candidate communications, scheduling, and logistics
• Maintain accurate records in our Ashby applicant tracking system (ATS)
• Partner with recruiters and hiring managers to identify process improvements
• Help manage recruiting operations projects (e.g., job descriptions, templates, metrics, reporting)
• Ensure all candidates have a positive and timely experience from first touch to close
• Greet candidates upon arrival, prepare meeting rooms before candidate onsite interviews
• Be a Brand Ambassador of Beacon at recruiting events to give a glimpse into the awesome culture that candidates look forward to joining
• Assist with data hygiene and integrity by verifying documents, data, and other details related to the hiring process

Recruiting Coordinator

Security Engineer

• Design and implement robust security architectures for applications and infrastructure.
• Develop and enforce security policies and procedures.
• Conduct regular security assessments, vulnerability scanning, and penetration testing.
• Ensure compliance with relevant security standards and regulations.
• Lead incident response efforts for security incidents.
• Integrate security best practices into the software development lifecycle (SDLC).
• Implement and manage security monitoring and logging solutions.
• Conduct security training and awareness programs for employees.
• Evaluate third-party vendors' security posture.
• Develop comprehensive security documentation.

Lead Software Engineer, Security

iOS Engineer

• Lead, mentor, and help grow a high-performing iOS engineering team by setting standards for technical excellence, architecture, and code quality.
• Drive the end-to-end technical vision for Murdock’s iOS platform, ensuring scalability, reliability, and performance in mission-critical use cases.
• Hands-on development, contributing to key modules, reviewing code, and modeling best practices in design and implementation.
• Design and build high-performance, real-time iOS applications using Swift, SwiftUI, UIKit, and modern frameworks.
• Collaborate with design and product teams to deliver intuitive, pilot-friendly interfaces.
• Integrate with internal and external APIs to enable secure, efficient data flow and real-time insights.

Lead Software Engineer – iOS/Mobile

• Contribute to the development and maintenance of Murdock’s iOS application, ensuring responsiveness and reliability.
• Support implementation of features that stream real-time data from aircraft in collaboration with backend engineers.
• Work with designers to build intuitive, effective UI that supports pilots in high-stakes environments.
• Integrate internal and external APIs to deliver real-time data within the app.
• Profile and improve app performance, particularly with time series or multimedia data.
• Participate in troubleshooting and fixing bugs to ensure app stability.
• Partner with engineers and product to scope and ship features that meet user needs.
• Stay current with mobile trends and tools to improve the product over time.

Software Engineer, iOS/Mobile – New Grad

Cloud Engineer

• Plan and enhance the cloud infrastructure to support high-volume, mission-critical systems, ensuring robustness and scalability.
• Design, implement, and maintain APIs for both internal and external data sources, including real-time data streaming from aircraft and support for web/iPad applications.
• Oversee bulk data ingestion processes into AWS services, ensuring efficiency and security in data handling.
• Set up and maintain Beacon AI’s cloud infrastructure using IaC tools like AWS CDK, with associated CI/CD and testing components.
• Provide expert-level troubleshooting and debugging for cloud applications, with a focus on uptime and reliability.
• Guide and mentor a team of engineers in developing and deploying serverless applications, fostering an environment of innovation and high performance.
• Analyze and optimize system performance for applications interfacing with time series data and multimedia streams, ensuring high reliability and responsiveness.
• Implement and maintain rigorous data security practices and protocols to protect sensitive information and comply with industry standards and regulations.

Technical Lead, Backend and Cloud Infrastructure

Backend Engineer

• Beacon AI: fast-moving team transforming aviation with an AI platform to make flying safer, more efficient, and more capable.
• Role: design, build, and maintain backend systems; contribute to serverless cloud infrastructure and real-time data processing.
• Backend Development: develop and maintain scalable backend services focused on performance, reliability, and testability.
• API Design: support design and development of APIs, including real-time data streaming from aircraft systems.
• Database Management: assist in developing database schemas and queries to support application functionality and data integrity.
• Data Pipelines: help build and maintain data pipelines that process and store aircraft data efficiently and reliably.
• Monitoring & Debugging: participate in diagnosing production issues and learning observability best practices to support uptime.
• Performance Tuning: contribute to optimizing backend performance for time series or streaming data.
• Work arrangement: hybrid role requiring presence in San Carlos, CA office at least three days per week.

Backend Software Engineer, New Grad

• Design, provision, and maintain AWS infrastructure using IaC tools such as AWS CDK or Terraform.
• Build CI/CD and testing for apps, infra, and ML pipelines using GitHub Actions, CodeBuild, and CodePipeline.
• Operate secure networking with VPCs, PrivateLink, and VPC endpoints. Manage IAM, KMS, Secrets Manager, and audit logging.
• Stand up and operate model endpoints using AWS Bedrock and/or SageMaker; evaluate when to use ECS/EKS, Lambda, or Batch for inference jobs.
• Build and maintain application services that call LLMs through clean APIs, with streaming, batching, and backoff strategies.
• Implement prompt and tool execution flows with LangChain or similar, including agent tools and function calling.
• Design chunking and embedding pipelines for documents, time series, and multimedia. Orchestrate with Step Functions or Airflow.
• Operate vector search using OpenSearch Serverless, Aurora PostgreSQL with pgvector, or Pinecone. Tune recall, latency, and cost.
• Build and maintain knowledge bases and data syncs from S3, Aurora, DynamoDB, and external sources.
• Create offline and online eval harnesses for prompts, retrievers, and chains. Track quality, latency, and regression risk.
• Instrument model and app telemetry with CloudWatch and OpenTelemetry. Build token usage and cost dashboards with budgets and alerts.
• Add guardrails, rate limits, fallbacks, and provider routing for resilience.
• Implement PII detection and redaction, access controls, content filters, and human-in-the-loop review where needed.
• Use Bedrock Guardrails or policy services to enforce safety standards. Maintain audit trails for regulated environments.
• Build ingestion and processing pipelines for structured, unstructured, and multimedia data. Ensure integrity, lineage, and cataloging with Glue and Lake Formation.
• Optimize bulk data movement and storage in S3, Glacier, and tiered storage. Use Athena for ad-hoc analysis.
• Manage infrastructure that deploys to and communicates with edge devices. Support secure messaging, identity, and over-the-air updates.
• Partner with product and application teams to integrate retrieval services, embeddings, and LLM chains into user-facing features.
• Provide expert troubleshooting for cloud and ML services with an emphasis on uptime and performance.
• Tune retrieval quality, context window use, and caching with Redis or Bedrock Knowledge Bases.
• Optimize inference with model selection, quantization where applicable, GPU/CPU instance choices, and autoscaling strategies.

Software Engineer, Cloud Infrastructure

About Beacon AI: We’re a fast-moving team of aviators, engineers, and operators on a mission to transform aviation with an AI platform that makes flying safer, more efficient, and more capable.
Role Overview: Backend Engineer to design, build, and maintain backend systems, serverless cloud infrastructure, real-time data processing, collaborate closely with engineers and product teammates.
Key Responsibilities: Backend Development: Contribute to development and maintenance of scalable backend services focused on performance, reliability, and testability.
API Design: Help design and implement APIs supporting internal tools and external applications, including real-time data streaming from aircraft systems.
Database Management: Collaborate on database schemas and queries to support functionality and data integrity.
Data Pipelines: Support buildout of data pipelines that process and store aircraft data efficiently and reliably.
Monitoring & Debugging: Diagnose production issues, improve observability, support system uptime.
Performance Tuning: Optimize backend performance, particularly for time series or streaming data.
Workplace: Hybrid role requiring San Carlos, CA office at least three days a week.

Software Engineer, Backend

• Beacon AI: AI platform to transform aviation, improving safety, efficiency, and capability.
• Company culture: small, focused teams owning products, shipping fast, learning quickly.
• Role overview: Senior/Staff Software Engineer, Backend to lead backend services, serverless cloud infrastructure, APIs, and data pipelines.
• Technical Leadership: set technical direction, mentor engineers, and lead hiring and projects.
• Backend Development: architect, design, and optimize backend services for mission-critical applications.
• API Development and Management: design and maintain secure, scalable APIs including real-time streaming for web/iPad apps.
• Database Management: develop and maintain schemas, queries, and ensure data integrity and performance.
• Data Pipeline Construction: build and optimize ingestion and processing pipelines ensuring reliability and security.
• Performance Optimization: analyze and improve responsiveness for time-series and multimedia streams.
• Advanced Troubleshooting: lead troubleshooting to ensure high availability of cloud applications.
• End-to-End Ownership: own product lines, milestones, and feature sets from concept to delivery.
• Collaborative Innovation: work with product management and cross-functional teams to deliver impactful solutions.

Senior/Staff Software Engineer, Backend

Infrastructure Engineer

About Beacon AI: We’re a fast-moving team of aviators, engineers, and operators on a mission to transform aviation with an AI platform that makes flying safer, more efficient, and more capable.
Role Overview: Lead design, implementation, and optimization of cloud and edge infrastructure; ensure reliable, scalable, secure deployment of applications to connected devices in the field.
Responsibilities:
- Design, implement, and maintain infrastructure for secure cloud-to-edge deployments using AWS Greengrass and other edge platforms.
- Build scalable provisioning, registration, and fleet management systems for Linux-based edge devices.
- Own CI/CD pipelines for cloud infrastructure and field devices, including OTA updates, rollbacks, and monitoring.
- Optimize infrastructure for cost, observability, and performance.
- Develop monitoring tools and contribute to incident response for IoT and cloud systems.
- Collaborate with embedded systems engineers to integrate edge applications with cloud services.

Cloud Infrastructure Engineer, IoT/Edge

• Cloud Infrastructure Setup and Maintenance: Design, provision, and maintain AWS infrastructure using IaC tools such as AWS CDK or Terraform.\n• Build CI/CD and testing for apps, infra, and ML pipelines using GitHub Actions, CodeBuild, and CodePipeline.\n• Operate secure networking with VPCs, PrivateLink, and VPC endpoints. Manage IAM, KMS, Secrets Manager, and audit logging.\n• LLM Platform and Runtime: Stand up and operate model endpoints using AWS Bedrock and/or SageMaker; evaluate when to use ECS/EKS, Lambda, or Batch for inference jobs.\n• Build and maintain application services that call LLMs through clean APIs, with streaming, batching, and backoff strategies.\n• Implement prompt and tool execution flows with LangChain or similar, including agent tools and function calling.\n• RAG Data Systems and Vector Search: Design chunking and embedding pipelines for documents, time series, and multimedia. Orchestrate with Step Functions or Airflow.\n• Operate vector search using OpenSearch Serverless, Aurora PostgreSQL with pgvector, or Pinecone. Tune recall, latency, and cost.\n• Build and maintain knowledge bases and data syncs from S3, Aurora, DynamoDB, and external sources.\n• Evaluation, Observability, and Cost Governance: Create offline and online eval harnesses for prompts, retrievers, and chains. Track quality, latency, and regression risk.\n• Instrument model and app telemetry with CloudWatch and OpenTelemetry. Build token usage and cost dashboards with budgets and alerts.\n• Add guardrails, rate limits, fallbacks, and provider routing for resilience.\n• Safety, Privacy, and Compliance: Implement PII detection and redaction, access controls, content filters, and human-in-the-loop review where needed.\n• Use Bedrock Guardrails or policy services to enforce safety standards. Maintain audit trails for regulated environments.\n• Data Pipeline Construction: Build ingestion and processing pipelines for structured, unstructured, and multimedia data. Ensure integrity, lineage, and cataloging with Glue and Lake Formation.\n• Optimize bulk data movement and storage in S3, Glacier, and tiered storage. Use Athena for ad-hoc analysis.\n• IoT Deployment Management: Manage infrastructure that deploys to and communicates with edge devices. Support secure messaging, identity, and over-the-air updates.\n• Analytics and Application Support: Partner with product and application teams to integrate retrieval services, embeddings, and LLM chains into user-facing features.\n• Provide expert troubleshooting for cloud and ML services with an emphasis on uptime and performance.\n• Performance Optimization: Tune retrieval quality, context window use, and caching with Redis or Bedrock Knowledge Bases.\n• Optimize inference with model selection, quantization where applicable, GPU/CPU instance choices, and autoscaling strategies.