Infinity Constellation

Full-stack Engineer

• Design and own the technical architecture of The SOURCE®
• Translate creative, brand, and agency workflows into scalable systems
• Build a platform that supports services today and SaaS tomorrow
• Make pragmatic decisions about build vs buy vs automate
• Ship fast, iterate often, and prioritize leverage over perfection
• Build internal tools that reduce scope creep and operational drag
• Integrate AI, automation, and workflow tooling where it actually adds value
• Establish technical standards, documentation, and patterns early
• Act as a thought partner to the founder on product vision and sequencing
• Help define what Radiance *should* and *should not* build
• Lay the groundwork for future engineering hires and contributors
• Own outcomes, not just code

Full Stack Engineer

Designer

• Lead design execution across brand, web, decks, social, and paid media
• Build and maintain scalable brand systems, style guides, and component libraries in Figma
• Translate creative direction and strategy into polished, production-ready assets
• Support web design with wireframes and layouts (Webflow build skills a plus, not required)
• Create internal Radiance marketing assets including case studies, decks, and visual narratives
• Ensure consistency, quality, and organization of assets within The Source®
• Participate in critiques, concept development, and collaborative reviews
• Balance speed and craft—shipping excellent work without over-polish

Associate Designer – Full-Time

AI Engineer

• Build production software with code and Supernal's proprietary platform, including backend services, data models, and CRUD applications
• Build and maintain integrations with external systems (APIs, webhooks, third-party tools, and data sources) that AI Employees can safely act on
• Design, implement, and deploy conversational agents, including multi-turn flows, state management, and tool usage
• Own end-to-end technical delivery for high-priority customer implementations, from architecture through production launch
• Translate customer requirements and SOWs into clear technical designs, execution plans, and deliverables
• Make and own architectural decisions across application design, API integrations, LLM orchestration, RAG design, and workflow decomposition
• Handle real-world voice system challenges including latency, interruptions, fallbacks, error handling, and failure recovery
• Write automated tests — unit tests for isolated logic and end-to-end tests for full system and user journey validation
• Apply solid error handling: distinguish retryable vs. fatal failures, surface meaningful error messages, and avoid silent failures
• Actively debug complex production issues across agent logic, prompts, integrations, and external dependencies
• Partner with delivery and product leadership to manage timelines, scope, and technical tradeoffs during implementation
• Review technical work for quality, scalability, and maintainability, setting a high bar for engineering excellence
• Define, document, and evolve best practices for building and delivering reliable AI Employees

Senior AI Engineer – Clients

Associate Designer – Long-Term Contract

Senior Designer – Radiance

Account Executive

• Own full-cycle deal execution from first touch to signed contract with global enterprise and government buyers
• Lead consultative discovery, solution scoping, and executive-level negotiations tailored to each buyer's regulatory context
• Build ROI-driven business cases that translate regulatory complexity into measurable outcomes
• Develop and iterate on playbooks, sequences, and repeatable systems that scale across verticals
• Own CRM discipline, forecasting accuracy, and pipeline velocity in HubSpot
• Represent Labrynth at conferences, industry events, and in-person meetings with senior buyers

• Design and build backend and platform features (APIs, services, integrations, and data flows)
• Own scoped projects end-to-end: requirements clarification, implementation, testing, rollout, and follow-through
• Write automated tests — unit tests for isolated logic and integration tests for system behavior
• Apply solid engineering fundamentals: error handling, retries/backoff where appropriate, and clear interfaces between components
• Debug production issues using logs/metrics/traces; drive issues to resolution and add safeguards to prevent recurrence
• Improve reliability and maintainability through refactors, performance improvements, and better observability
• Communicate progress, tradeoffs, and risks clearly to engineering and cross-functional partners
• Contribute to team standards: code review, documentation, and best practices that keep builds predictable and supportable

Junior AI Engineer, Clients

• Build and extend multi-agent AI pipelines for regulatory document analysis, gap identification, and pre-draft generation
• Work with export-controlled materials in a compliant environment
• Develop and improve GraphRAG-based knowledge retrieval systems using Neo4j
• Integrate and orchestrate LLM calls for document processing workflows
• Collaborate with engineers and directly with clients to understand domain requirements and translate them into technical solutions
• Contribute to platform architecture as we modularize and scale our systems across multiple regulatory domains

• You'll design and maintain the infrastructure that powers client deliverables - ingestion pipelines, data models, transformation layers, and the orchestration that keeps it all running.
• You'll build and ship AI applications - not prototype them. That means writing production-grade code, designing agent workflows, and integrating LLMs into systems where accuracy and reliability actually matter.
• You'll architect agent systems with clear evaluation criteria, human-in-the-loop checkpoints where they matter, and monitoring that tells you when something goes wrong.
• You'll own deployment end-to-end - containerized services, Kubernetes orchestration, cloud infrastructure, and the CI/CD pipelines that keep everything moving.
• As a founding engineer, you'll help identify what we build repeatedly across engagements and turn it into durable internal infrastructure - shared data models, evaluation frameworks, deployment patterns.

Founding AI Engineer

Engineering Manager

• Lead multiple Mason pods and own delivery outcomes
• Translate ambiguous requests into clear plans and execution strategy
• Set and enforce production-quality standards for Mason builds
• Serve as technical escalation for difficult problems
• Establish and evolve team processes
• Drive prioritization and capacity planning across pods
• Partner with Delivery Leads and stakeholders
• Hire and build the team
• Manage performance and develop leaders within the Mason org

Engineering Manager – AI

• Drive platform architecture decisions and align the team on scalable patterns and long-term maintainability
• Review a high volume of code, design docs, and architectural proposals for scalability, reliability, security, and operability
• Be a technical mentor and force multiplier: unblock engineers, raise the bar on production readiness, and establish platform best practices
• Own and evolve the core backend platform (Django/DRF/ASGI) performance and correctness
• Scale async execution across Celery + Dramatiq + Temporal/Cortex; implement resilient workflow patterns (retries, circuit breakers, graceful degradation)
• Optimize PostgreSQL/pgvector (query tuning, connection pooling) and caching strategies
• Maintain and improve Kubernetes deployment infrastructure (GKE, Helm, Terraform/OpenTofu) and CI/CD + rollout strategies. Own KEDA autoscaling policies and resource allocation across worker pools.
• Own reliability of RabbitMQ, Redis, and PostgreSQL infrastructure; lead incident response and post-mortems
• Extend OpenTelemetry + Datadog instrumentation, dashboards, alerts, and SLOs; profile and reduce latency/memory bottlenecks

Staff Software Engineer

• Ship user-facing agent experiences end-to-end: prototype → production → iteration based on real usage.
• Architect and implement stateful agent systems (workflows, tool calling, memory, retrieval, and human-in-the-loop where needed).
• Build voice features end-to-end where they unlock value: realtime speech agents, voice UI/UX, prompt/audio routing, and guardrails for safe tool execution.
• Build/own an evaluation harness:
• - curated test sets + scenario suites
• - automated scoring / rubric-based graders
• - prompt/model/version tracking
• - canary + A/B experimentation and safe rollout patterns
• Design data + retrieval pipelines:
• - chunking, enrichment, metadata strategy
• - hybrid retrieval (vector + keyword + structured filters)
• - re-ranking, caching, and latency optimization
• - multi-tenant safety and data isolation
• Integrate with and extend our platform primitives:
• - Django/DRF/ASGI services
• - async execution + queues + workflow orchestration
• - PostgreSQL + pgvector
• - Kubernetes deployments, autoscaling, and cost controls
• Establish engineering rigor for agents:
• - observability (traces, spans, structured logs)
• - reliability patterns (timeouts, retries, circuit breakers, graceful degradation)
• - security/privacy controls for data access and tool execution