• Own end-to-end development of the Enterprise Voice AI Agent: architecture, features, integrations, and ongoing maintenance • Build and tune AI voice agent pipelines — including speech recognition, natural language understanding, dialogue management, and text-to-speech • Develop and maintain the frontend (React/Next.js) and backend (Node.js/Python) components of the platform • Integrate the voice agent with the portfolio company's existing systems (CRMs, telephony platforms, APIs) and their clients' infrastructure • Maintain a prioritized backlog and drive features and improvements to production independently • Serve as the main technical liaison with the portfolio company — conducting requirements sessions, reviews, and status updates in Dutch • Translate the portfolio company's business needs into technical specifications and working software • Write client-facing documentation, release notes, and user guides in Dutch • Manage expectations proactively and communicate timelines, risks, and decisions clearly • Design and integrate AI agents to automate testing, debugging, performance monitoring, and feature prototyping • Supervise agentic AI outputs to ensure they align with product goals, code standards, and security requirements • Continuously improve platform reliability, call handling quality, and voice agent performance • Apply AI tools to documentation, observability, and operational workflows

Role Description If you've worked alongside hardware teams, you know the damage that results from a missed change request or critical context that was never relayed to the right person. Reflow exists to close that gap. We're building the first AI-powered platform built for hardware product development, one that listens across the tools teams already use, maintains a structured picture of every program, and proactively coordinates across disciplines when things inevitably change. This is an early role for a hands-on AI engineer to design and build the agents at the core of our platform, backed by a parent company with deep roots in engineering and manufacturing. You'll own the AI systems that set our product apart: agents that understand hardware workflows, anticipate problems, and take action on behalf of engineering teams. What You'll Do - Designing, building, and iterating on LLM-powered agents that coordinate across engineering disciplines, surface project risks, and generate structured deliverables (proposals, SOWs, status reports) - Owning the agent orchestration layer (currently LangChain DeepAgent) and continuously evaluating whether to extend, replace, or supplement it as new frameworks and patterns emerge - Implementing robust tool-use patterns that connect agents to external systems (project management tools, CAD/PLM platforms, communication channels) via APIs and integrations - Designing and tuning prompts, chains, and retrieval strategies to maximize agent reliability, accuracy, and usefulness across diverse hardware project contexts - Building evaluation and observability infrastructure for agent performance, including tracing, cost tracking, latency monitoring, and automated quality benchmarks - Developing streaming agent interfaces that surface real-time progress, reasoning transparency, and proactive alerts to end users - Staying current with rapid advances in LLMs, agent frameworks, and related tooling, and translating that awareness into actionable recommendations for the team - Collaborating with frontend engineers on the UX of AI-powered features and with backend engineers on data pipelines and API design - Contributing to AI architecture decisions, code reviews, and engineering best practices Qualifications - 5+ years of production software engineering experience, with 2+ years focused on bringing LLM-based applications or agent systems to market - Demonstrated proficiency using AI coding tools (Cursor, Copilot, Claude, etc.) to accelerate development - Hands-on experience building and deploying agentic systems using frameworks such as LangChain/LangGraph, CrewAI, AutoGen, or custom orchestration - Strong understanding of LLM fundamentals: prompt engineering, function/tool calling, retrieval-augmented generation (RAG), context window management, and token economics - Proficiency with Python in production environments - Experience integrating LLM-powered features with external APIs, databases, and third-party tools - Experience designing and operating background job / async task pipelines (Celery, RQ, Temporal, or similar) for long-running agent runs and reliable retries - Experience building multi-agent systems with planning, delegation, and inter-agent communication patterns - Demonstrated ability to evaluate and adopt new AI tools and frameworks quickly, with a track record of staying ahead of a fast-moving field - Strong software engineering fundamentals: clean architecture, testing, version control, and code review practices - Ability to balance rapid experimentation with production-grade reliability Requirements - Direct experience with LangChain's DeepAgent or LangGraph for multi-step agent orchestration - Background in evaluation frameworks for LLM outputs (automated scoring, human-in-the-loop evaluation, regression testing for prompts) - Familiarity with vector databases and embedding pipelines (Pinecone, Weaviate, pgvector, or similar) - Experience with model serving infrastructure, fine-tuning workflows, or model selection/routing strategies - Understanding of authentication/authorization patterns (OIDC, JWT) and secure handling of user data in LLM contexts - Background in B2B SaaS platforms, project management tools, or technical collaboration products - Familiarity with hardware development, engineering workflows, or project management concepts (phases, gates, dependencies, requirements traceability) - TypeScript / React fluency, enough to pair with frontend engineers on streaming agent UIs and reasoning-transparency surfaces Benefits - Full health/dental/vision, bonus program, generous 401K, paid time off, annual learning stipend - Participation in Re:Build's LTIP equity program and opportunity for founder equity in potential spin-out Compensation & Location - Location: Remote-first, with preference for candidates in Boston, Seattle, Los Angeles, or other cities with Re:Build offices - Compensation: Base salary range of $143,000 to $215,000 with performance bonus and long-term incentive plan offered. Potential equity stake under independent spinout scenario.

• Proactively Identify AI Opportunities • Innovate with AI Solutions • Maximize Data Potential • Scalable AI/ML Infrastructure • MLOps & AI Automation • Cross-functional collaboration • Stay Ahead of AI Trends

Role Description Firestorm is building the next generation of uncrewed aircraft and the advanced manufacturing systems that deliver them at speed. The Software Integration & Operations department owns the software layer that spans factory floor to cloud - the applications, automation, edge systems, and intelligence that make it possible to iterate product designs, automate advanced manufacturing, and scale production with uncompromising quality and rigor. Firestorm's manufacturing platform has an intelligence layer - a planning workbench, a simulation engine, an analytics surface, and eventually an AI assistant - that turns operational data into better decisions. Building this layer requires applied math, pragmatic ML systems engineering, and a deep understanding of the manufacturing domain it serves. As an Applied AI & Optimization Engineer, you will own the algorithms and systems behind this intelligence layer. Near-term, your focus is the planning workbench and simulation engine: - Optimization algorithms for work order scheduling, resource allocation, and conflict resolution. Longer-term, you will lead integration of open-source and custom LLMs into the platform's AI assistant - including air-gapped and on-edge deployments for DoD contexts. What You'll Do: - Own the optimization algorithms behind the planning workbench and simulation engine - scheduling, resource allocation, constraint satisfaction, conflict detection. - Design and implement the analytics layer of the platform: defect trends, yield analytics, throughput modeling, and operational intelligence. - Lead the platform's AI assistant integration: selecting, evaluating, deploying, and fine-tuning open-source or custom LLMs for cloud, air-gapped, and on-edge contexts. - Productionize optimization and ML systems in partnership with full-stack and infrastructure engineers - reliable services the platform depends on, not prototypes. - Partner with domain experts in manufacturing engineering, quality, and planning to ground models and algorithms in real operational constraints. - Evaluate and advocate for build-vs-buy decisions across optimization libraries, ML tooling, and model vendors. Qualifications - 5+ years of engineering experience with substantial applied optimization, operations research, or ML systems work. - Deep proficiency in Python; fluency with at least one optimization framework - MILP solvers, constraint solvers, OR-Tools, or equivalent. - Track record of productionizing algorithmic systems - you have shipped optimization or ML into real users' hands, not just research artifacts. - Strong applied math foundation: combinatorial optimization, heuristics, or statistical modeling relevant to scheduling and resource allocation problems. - Demonstrated ability to partner with domain experts and translate operational constraints into model formulations. - Demonstrated history of holding yourself and your teammates to a high standard, even when it creates discomfort. Preferred Qualifications - Prior experience building scheduling, planning, or resource allocation systems for manufacturing, logistics, or similar domains. - Hands-on experience deploying or fine-tuning open-source LLMs (Llama, Mistral, or similar) for constrained environments. - Background with air-gapped or on-edge model deployment. - Familiarity with discrete-event simulation or agent-based modeling. - Prior experience in defense, aerospace, or regulated industry applications.