AI-Native Fullstack SDE at Bullwhip
TL;DR
We're looking for an AI-Native Fullstack Software Development Engineer to join our fast-moving, high-impact team. In this hands-on role, you'll contribute across our backend (Python/Django) and frontend (JavaScript/TypeScript, Vue.js/Nuxt) stacks, working closely with senior engineers and product stakeholders to ship features, improve system performance, and help scale our platform — with AI-assisted development as a core part of how you work, not a side experiment.
This role is ideal for someone with 2–5 years of professional experience and a strong foundation in fullstack development, CI/CD best practices, and cloud infrastructure (particularly GCP), who has fluently integrated coding agents, LLM APIs, and AI-powered tooling into their daily workflow.
Ambitious, detail-oriented developers who treat AI as a force multiplier — and are eager to shape the future of our product — should apply by sending a statement of interest and resume to talent@bullwhip.io.
What You’ll Do
As a Principal SDE at Bullwhip, your role will span the following:
Backend & Data Engineering
Build and maintain Python/Flask/Django services that support performance analytics, reporting, and client integrations.
Work with Postgres, BigQuery, and Pub/Sub to build reliable and scalable batch and real-time data pipelines.
Develop and manage data pipelines for in-house and client data at scale.
Lead new client integration / onboarding efforts, including improving data structure normalization and automation where helpful.
Contribute to data validation and ingestion logic to ensure data quality across systems.
Support integrations with tools like GCP Workflows, Cloud Run Jobs, and AlloyDB as the stack evolves.
Frontend Development
Build and maintain frontend Node/JS services that support realtime analytics, yield optimization and attribution.
Develop frontend components for our Vue.js/Nuxt-based analytics application (Beacon).
Contribute to browser-based tools (e.g., Chrome Extensions) for on-page analytics, DOM interaction, and URL rewriting.
Optimize performance and maintain compatibility across diverse client environments.
Infrastructure & CI/CD
Contribute to CI/CD workflows using GitHub Actions, Docker, and GCP-native tools.
Help automate client onboarding workflows and repetitive integration tasks.
Participate in performance monitoring, logging, and observability improvements.
Team Collaboration
Work closely with senior engineers, product managers, and client-facing teams to deliver features and debug production issues.
Follow modern development practices and contribute to internal documentation.
Learn and grow under the mentorship of Principal and Staff-level engineers.
AI-Native Development
Successful candidates will have a demonstrated track record of the following competencies:
Using coding agents (Claude Code, Cursor, Copilot, or equivalent) as a daily part of feature development, debugging, refactoring, and code review.
Building internal tooling, automations, and prototypes that leverage LLM APIs (Anthropic, OpenAI, etc.) where they meaningfully accelerate the team or product.
Applying strong judgment on when AI assistance adds leverage vs. when it introduces risk — review and own all generated code as if you wrote it yourself.
Contributing to internal AI workflow conventions: prompt patterns, agent configurations, MCP integrations, and repo-level context (CLAUDE.md, .cursorrules, etc.).
Helping evaluate and adopt emerging AI dev tools, and share learnings with the rest of engineering.
Qualifications
Core Experience
2–5 years of professional experience as a fullstack or backend software engineer.
Strong proficiency in Python/Django and Node/JavaScript/TypeScript.
Experience with at least one modern frontend framework—Vue.js, Nuxt, or React.
Working knowledge of SQL and experience with Postgres and/or BigQuery.
Solid fundamentals in systems design, data structures, and version control (Git).
Demonstrated fluency with AI coding tools (Claude Code, Cursor, Copilot, Windsurf, or similar) in real production work — not just experimentation.
Comfort calling LLM APIs directly and reasoning about tradeoffs (model choice, context windows, structured outputs, tool use, cost/latency).
Cloud & DevOps
Experience deploying to or building on Google Cloud Platform (GCP).
Exposure to Cloud SQL, Pub/Sub, BigQuery, GCS, and IAM.
Familiarity with CI/CD pipelines and containerization tools like Docker.
Problem Solving & Communication Skills
Proven ability to troubleshoot bugs, track down root causes, and write maintainable solutions.
Effective communicator—able to document decisions clearly and collaborate across functions.
Bonus Points
Experience with website event tracking, or in-browser SDKs.
Exposure to affiliate marketing systems or digital advertising pipelines.
Experience building or maintaining Chrome Extensions.
Familiarity with tools like Airflow, Datastream, or large-scale database operations.
Interest or experience in eCommerce, web scraping, or structured data extraction.
Contributions to open-source projects or personal technical blogs.
Experience building agentic workflows, RAG systems, or LLM-powered features in production.
Familiarity with MCP (Model Context Protocol) servers or similar agent-tooling standards.
Contributions to AI dev tooling, prompt libraries, or evals frameworks.
A point of view on where AI-assisted development is going and how teams should adapt.
You’ll Thrive Here If You…
Enjoy building across the stack and learning new systems quickly.
Are comfortable in fast-paced environments and adaptable to change.
Take pride in writing clean, reliable code and improving systems iteratively.
Value ownership, transparency, and working on a collaborative team.
Treat AI tools as leverage, not a crutch — and can tell the difference.
Stay current on the rapidly shifting AI tooling landscape without getting distracted by every shiny release.