Flock Safety - Software Engineer III, Fullstack/Investigative Agent

• Love for coding and continuous learning, especially in the rapidly evolving LLM space • Resourceful problem-solver mindset: excel in ambiguous situations and take initiative to define product direction • Strong TypeScript / Node / Express skills for web services and API design (REST, SSE, WebSockets for streaming) • TypeScript / Node / Express • Modern web framework expertise (React / TypeScript preferred), particularly for conversational UI and chat interfaces • (React / TypeScript preferred) • Hands-on LLM experience: OpenAI/Anthropic/Gemini APIs, prompt engineering, streaming responses, and conversation context management • OpenAI/Anthropic/Gemini APIs • Familiarity with agentic patterns: function calling, tool use (MCP), and orchestrating multi-step workflows • API integration skills: consume existing APIs or design new ones to ground AI in investigative data • Database confidence: PostgreSQL and sophisticated SQL for data retrieval • Cloud infrastructure basics: Docker, Kubernetes (Helm), AWS services (S3, SQS, API Gateway) • Docker, Kubernetes • AWS services • Product-minded: translate user feedback into technical requirements and make pragmatic tradeoffs • Bonus points for: LLM evaluation tools (LangSmith, Langfuse), vector search/RAG, microservices architecture, or Terraform • LLM evaluation tools • 90 Days at Flock • 90 Days at Flock • The First 30 Days: • The First 30 Days: • Onboard and Integrate: • Familiarize yourself with Flock's mission, investigative workflows, and how customers use our platform today • Pair with engineers across Cloud Software and ML teams to understand existing APIs, data models, and system architecture • Build relationships with key stakeholders to understand their capabilities and constraints. Meet with members of: • Machine Learning (agentic systems, model serving) • Data Engineering (investigative datasets, pipelines) • Platform teams (APIs, infrastructure) • Product and Design (customer needs, UX direction) • Ship Early and Learn: • Complete a first-day push to production • Pick up initial sprint tickets: bug fixes, small UX improvements, or API integrations • Participate in customer feedback sessions to understand investigator workflows and pain points • The First 60 Days: • The First 60 Days: • Build the Foundation: • Deliver core conversational UI components and establish patterns for chat interfaces • Implement backend orchestration for LLM interactions and tool calling • Stand up observability for the AI system (logging, tracing, basic metrics) • Work with ML team to integrate agentic workflows and refine prompt strategies • Demonstrate Velocity: • Own end-to-end features that connect UI, backend orchestration, and data integrations • Collaborate with Product to rapidly iterate based on early user testing • Propose technical improvements to chat quality, performance, or reliability • 90 Days & Beyond: • 90 Days & Beyond: • Drive Product Impact: • Lead development of a core Night Shift capability that demonstrably improves investigator efficiency • Represent the team in cross-functional initiatives, balancing zero-to-one experimentation with engineering best practices • Establish patterns for testing and quality in an evolving AI product • Shape the Direction: • Influence product roadmap through technical insights and customer feedback • Mentor team members on LLM integration patterns or full-stack best practices • Own a domain area (e.g., conversation management, data grounding, streaming architecture) • The Interview Process • We want our interview process to be a true reflection of our culture: transparent and collaborative. Throughout the interview process, your recruiter will guide you through the next steps and ensure you feel prepared every step of the way. To check out our interview stages and how you should prepare visit experiences on our careers page.