• Ship Features End-to-End: You will start with smaller, scoped features (e.g., "Add a 'Dark Mode' toggle" or "Build a new API endpoint for user settings") and quickly graduate to owning complex workflows.
• Fix Bugs & Optimize: You will dig into the codebase to find why a page is loading slowly or why a specific edge case is breaking the app, and you’ll fix it.
• Learn the "AI Stack": You will get hands-on experience integrating Large Language Models (LLMs) into a web application, handling streaming responses, and managing context windows.

• Strong Fundamentals: You understand Data Structures and Algorithms. You know why a Hash Map is faster than a Linked List for lookups.
• Project Experience: You didn’t just do the homework. You have a GitHub with side projects, hackathon submissions, or a capstone project where you built a full web app (Frontend + Backend + DB).
• Grit: Startups are hard. Requirements change. Sometimes things break. You need to be resilient and eager to solve the puzzle.
• BS or MS in Computer Science, or equivalent intensive bootcamp experience.

• Visualize the "Brain": Build interfaces that demystify the AI, showing users exactly how it thought through a problem and why it made specific decisions.
• Render Complexity: Design and implement visualizations for complex analysis methods that handle massive density without dropping a frame.
• Engineer Nuance: Build granular, local-level feedback mechanisms. You'll move beyond "thumbs up/down" to create interactions that allow users to coach the AI on specific details efficiently.
• Modernize the Core: Lead high-impact architectural migrations—like moving from REST to GraphQL—to ensure our data layer is as snappy and efficient as our UI.
• Polish the Physics: Make interactions feel instant. You will obsess over latency, optimistic updates, and the "snap" of the application.

• A Systems Thinker: You don't just write components; you design systems. You think about state management, data synchronization, and the event loop before you write a line of UI code.
• Obsessed with "Feel": You know the difference between 16ms and 50ms. You care about scroll performance, input latency, and optimistic UI. You can debug a memory leak or a layout thrash as easily as you can center a div.
• Pragmatically Robust: You write code that is boring in the best way. You value readability and maintainability over clever one-liners. You build interfaces that don't break when the network is slow or the data is weird.
• Design Fluent: You speak the language of designers. You can take a high-fidelity mock and implement it with pixel-perfect precision, or fill in the gaps when the mock is low-fidelity.
• A Force Multiplier: You elevate the team. You write documentation, review code with empathy and rigor, and mentor junior engineers to help them see the matrix.

• Deep, fundamental knowledge of TypeScript and the browser environment (not just the frameworks).
• A track record of shipping complex web applications (e.g., creative tools, data visualizations, IDEs, or high-density dashboards).
• Experience with local-first architectures, complex undo/redo stacks, or CRDTs is a massive plus. A portfolio of work that demonstrates both engineering complexity and design simplicity.

• Advanced Agentic Systems: Architect and implement complex agent behaviors, leveraging multi-agent hierarchies, multi-trajectory inference, and RL environments to solve open-ended tasks.
• Automated Optimization: Move beyond manual prompt engineering. Build and deploy automated optimization pipelines (like DSPy or custom optimizers) to systematically improve agent performance.
• Rigorous Evaluation: Design and own the evaluation stack. Implement LLM-as-a-judge frameworks and domain-specific metrics to ensure our agents are reliable, safe, and improving over time.
• Applied Research: Translate the latest papers on RAG techniques and cognitive architectures into production-ready code that drives our product forward.

• From Paper to Prototype: Read a new paper on tree-of-thought reasoning in the morning and have a working implementation testing against our benchmarks by the afternoon.
• Optimize the "Brain": Diagnose why an agent is hallucinating in a specific edge case and deploy a targeted fix via context engineering or fine-tuning, validating it with regression tests.
• Architect the Swarm: Design a communication protocol for a hierarchy of agents where a "Manager" agent effectively delegates sub-tasks to specialized "Worker" agents without getting stuck in loops.
• Cut the Fat: Reject a complex research proposal because a simpler, heuristic-based approach solves 90% of the user's problem with 10% of the compute.

• Deep AI Fluency: You are steeped in the latest LLM research. You understand the internals of Transformers, but more importantly, the emergent behaviors of agentic systems.
• Research Agility: You can quickly digest papers on RAG, multi-agent collaboration, and RLHF, and judge their applicability to our stack.
• Eval Obsession: You know best practices for evaluation like the back of your hand. You don't trust a prompt until you have the metrics to back it up.
• Product Intuition: You grok business concepts. You propose creative technical solutions that align with commercial goals, not just academic curiosity.
• MVP Mindset: You move fast. You prioritize speed of iteration and learning over theoretical perfection.

• Architect Multi-Tenancy: Design and implement robust data isolation and RBAC structures to ensure enterprise customers can safely deploy agents across diverse organizational units.
• Scale the Workload: Build and manage the orchestration layer that handles high-concurrency agentic workloads, utilizing containerization (Docker/K8s) to ensure seamless scaling.
• Hardened Security: Take ownership of the platform's security posture, from implementing sophisticated Auth patterns to leading pen-testing efforts and remediating vulnerabilities.
• Interface Evolution: Lead the debate on GraphQL vs. REST for complex data fetching, while implementing Server-Sent Events (SSE) or WebSockets for real-time AI streaming.
• Optimize the Edge: Design rate-limiting and circuit-breaking strategies that protect our infrastructure (and our LLM spend) from non-deterministic agent behavior.
• Product Onboarding: Build friction-free, enterprise-grade onboarding flows that simplify complex configuration for non-technical administrators.

• Senior Pedigree: You have 7+ years of experience, with a significant portion spent building and maintaining distributed systems or enterprise SaaS platforms in the US/Canada market.
• Security Mindset: You don't view RBAC or pen-testing as an afterthought. You understand OIDC, SAML, and the nuances of securing LLM-based applications against prompt injection and data leakage.
• Infrastructure Fluency: You are a master of modern DevOps. You know how to containerize a complex microservice architecture and keep it running at 99.9% uptime.
• Opinionated Tech Selection: You can articulate exactly when to use a relational DB vs. a vector store, and when to opt for a GraphQL schema over a traditional RESTful API.
• The "Lead from the Front" Mentality: You write clean, testable code that serves as the gold standard for the rest of the engineering team.

• Languages: Mastery of Python and TypeScript.
• Architecture: Deep experience with multi-tenant SaaS architecture, RBAC/ABAC, and Auth0/Okta integration.
• Communication: Proven experience implementing SSE, WebSockets, REST vs. GraphQL, and high-performance frontends.
• Infrastructure: Proficiency with Docker, Kubernetes, Terraform, and cloud providers (AWS/GCP).
• AI Exposure: While you are an engineering specialist, you have a working knowledge of RAG pipelines and how to optimize the latency of LLM responses in a production UI.

• Architect GenAI Orchestration: Design and build complex, reliable LLM pipelines. You will go far beyond basic prompts to engineer multi-agent DAGs, advanced RAG architectures, and custom tool-use integrations that operate predictably in production.
• Embed Domain Intelligence: Translate complex statistical and experimental methods specific to Market Research into algorithmic workflows that our AI agents can execute flawlessly.
• Drive Product Design & UX: Own the end-to-end user experience. You won't just build the backend systems; you will design how users interact with non-deterministic AI outputs, ensuring the experience feels intuitive, transparent, and trustworthy.
• Lead & Mentor: Act as a technical pillar for the founding team. You will make foundational architecture decisions, establish best practices for AI evaluation, and eventually mentor junior product engineers.

• You are a Battle-Tested Builder: You have 6+ years of software engineering experience, with a proven track record of taking complex, data-heavy products from zero to one.
• You know the "AI Stack" in Production: You have deep, hands-on experience building with LLMs in production environments. You understand the nuances of context management, latency optimization, evals, and agentic orchestration (LangChain, LlamaIndex, custom frameworks).
• You have Domain Fluency: You have a strong grasp of the statistical methods, experimental designs, and data modeling required in Market Research, and you know how to build software that respects those principles.

• Agentic Infrastructure: Select and implement agentic frameworks to maximize configurability and observability while minimizing switching costs for future LLMs.
• Security & Reliability: Design robust rate-limiting and authorization stacks. In an agentic world, verifying who is acting is as critical as what they are doing.
• High-Volume Data Pipelines: Own cloud ingestion and crawl pipelines. You'll build systems handling messy, unstructured data at scale to reliably feed our agents.
• API Hygiene: Drive efficiency and strict DevOps standards. Lead the migration from REST to GraphQL to enable faster frontend iteration without backend bottlenecks.

• Debate the Stack: Evaluate open-source agent frameworks. Decide to fork, wrap, or build from scratch to avoid ecosystem lock-in.
• Architect for Scale: Design a distributed rate-limiter to protect downstream LLM providers without degrading the power-user experience.
• Debug the "Black Box": Trace failed actions through crawl, embedding, and inference layers, and ship immediate fixes.
• Elevate the DX: Refactor legacy endpoints into flexible GraphQL schemas, unblocking the product team.

• Experience: 5+ years of backend engineering experience, with a track record of high-impact technical ownership.
• Autonomy: You don't need hand-holding. You spot problems, design solutions, and execute.
• System Design: You have architected systems handling significant scale or complexity. CAP theorem is a reality for you, not just an interview topic.
• Data & AI Fluency: You understand data ingestion and ML infrastructure. You know how to build the pipes that feed the models.
• Practicality: You disdain over-engineering. You write clean, testable code but never lose sight of shipping the product.

• Languages: Python (AI/Backend), Go (High-performance services), TypeScript.
• Infrastructure: AWS/GCP, Kubernetes, Terraform.
• Data: Postgres, Vector Databases (Pinecone/Weaviate), Redis.
• API: GraphQL, REST, gRPC.

• Forensic Evaluation: Review agent outputs (e.g., classification of social verbatim based on topic, relevancy) and score them on logic, bias, accuracy, and commercial viability.
• Gap Analysis: Identify why the AI failed. Did it miss a cultural nuance? Did it confuse correlation with causation? Did it ask a leading question?
• Ground Truth Creation: Create "perfect" examples and documentation of specific research tasks to feed into our training datasets (few-shot prompting).
• Engineering Feedback Loop: Translate "analyst intuition" into structured feedback that our Engineering and ML teams can use to fine-tune the models.

• Preferably 2–5 years of hands-on experience in Market Research and Insights or Social Listening.
• The "500 Reps" Standard: You have personally designed, executed, or delivered over 500 quantitative or qualitative studies in your career.
• Big Agency Pedigree: Proven tenure at top-tier firms (e.g., BCG, Kantar, Sprinklr, etc.) or client-side experience at Fortune 500 brands.
• Uncompromising Standards: You are the person who used to catch the errors that junior researchers missed. You have a reputation for being a "tough grader" or "bar raiser."

• Shape the Future: Stop fearing AI and start teaching it. Your intuition will literally become part of the code base for the next generation of research tools.
• High-Leverage Work: No more chasing clients, fielding costs, or formatting PowerPoint slides. This is pure intellectual application of your expertise.
• Flexible Engagement: We offer flexible hours and project-based work that fits around other consulting gigs or lifestyle requirements.

Please include the following in your application:

• CV
• Link to your LinkedIn profile
• Most recent transcript
• Qual/Quant study portfolio consisting of an anonymized inventory of prior projects (e.g., "Conducted a large conjoint analysis across 25,000 participants to determine X in industry Y for a company of scale ABC in country Z")

• End-to-End Testing: Design and execute comprehensive tests across our AI platform, covering an exhaustive range of angles, edge cases, and scenarios across our entire product suite.
• Full-Stack Troubleshooting: Proactively detect, document, and trace issues across the stack—from UI/frontend glitches to backend data mapping and output population.
• Process Engineering: Collaborate with the team to establish and refine the QA pipeline from the ground up.

• Uncompromising Standards: You are highly diligent, detail-oriented, and refuse to lower your bar for quality.
• Analytical Thinker: You possess strong critical thinking skills and a proven ability to build structured workflows and Standard Operating Procedures (SOPs) for QA.
• Domain Knowledge: A solid understanding of market research products.
• Technical Proficiency: Comfortable navigating technical tech stacks, with a strong foundational knowledge of infrastructure and APIs.
• A tracked academic record in the fields of Computer Science, Information Technology, or Data Science.

• System Infrastructure: Solid understanding of technical infrastructure, cloud environments, and system architecture to effectively navigate and test backend processes.
• API Proficiency: Hands-on experience interacting with and testing APIs, including verifying data mapping, integration points, and endpoint responses.

Please include the following in your application:

• CV
• Link to your LinkedIn profile
• Most recent transcript