> Markdown version of [/jobs/ext/4691433-sr-fullstack-platform-engineer-backend-focus](https://www.wearedevelopers.com/jobs/ext/4691433-sr-fullstack-platform-engineer-backend-focus). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Fullstack Platform Engineer - (Backend Focus) - **Company:** Rough House Games, Inc. - **Location:** Malibu, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $140,000 - $180,000 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Automated Storage and Retrieval Systems, Cloud Computing, Code Generation, Customer Data Management, Data Security, Cursor, Software Debugging, Distributed Systems, Django Web Framework, Github, Python (Programming Language), TypeScript, Enterprise Software Applications, ReactJS, Claude Code, Multi-Agent Systems, Indexer, Backend, Kubernetes, Low Latency, Data Management, Front End Software Development, Automation Anywhere - **Published:** November 12, 2026 - **Apply:** https://www.careerbuilder.com/job-details/sr-fullstack-platform-engineer-backend-focus-los-angeles-ca--cce0c2ee-0575-4135-8abf-1a249655be1c ## About the Role * Strong backend and platform experience. You are credible in Python/Django, APIs, distributed systems, cloud infrastructure, Kubernetes, queues, jobs, and production reliability. * Fullstack production range. You are comfortable enough with TypeScript, React, and frontend product surfaces to build, debug, or shape the UI that exposes your backend work. * High agency and high standards. You do not wait for a perfect spec. You clarify the problem, find the constraint, make progress, and raise the quality bar as you go. * Platform instincts. You think in contracts, interfaces, failure modes, permissions, observability, and lifecycle. You know the difference between a feature that works once and a primitive that other people can safely build on. * Good taste in abstraction. You do not over-framework the first version, but you can see when repeated customer work wants to become a platform capability. * Taste in customer experience. You can tell when a complex workflow is technically correct but still confusing, brittle, or hard to trust. You care about making advanced software feel legible, fast, and empowering. * AI-first engineering habits. You use tools like Claude Code, Cursor, Codex, or similar systems to move faster and think at a higher level. You still understand the code you ship, review generated work carefully, and know when to slow down. ## Description This is a Fullstack Platform Role for someone who likes deep systems, clean abstractions, and product surfaces that make hard infrastructure feel usable. You will work across workflow execution, agent orchestration, pipeline-as-tool contracts, customer data access, retrieval, permissions, observability, and the product surfaces that expose those capabilities to real users. Some weeks the important work will be backend architecture. Some weeks it will be the API and event contract that makes the frontend possible. Some weeks it will be a React surface that helps a customer understand what happened inside a distributed AI workflow. The through-line is platform leverage plus customer experience: building primitives that make Salt more trustworthy, more composable, easier for internal teams to extend, and dramatically better for customers to use. We are an AI-first engineering team building AI tools for modern, forward-thinking companies. That does not mean accepting AI-generated slop at higher velocity. It means using AI to increase your leverage while keeping your standards intact. The right person can use agents, code generation, and modern tooling aggressively without surrendering authorship, judgment, or accountability. AI should make you faster, not less discerning. We are hiring engineers whose taste survives contact with AI-generated code. What Good Looks Like Here We are closer to Linear than to a traditional enterprise software team in how we think about quality. Craft is not a decorative layer, and it is not limited to the frontend. Quality is the whole customer experience. That means: * You know what good looks like. You can tell when a workflow is technically correct but still wrong: too vague, too slow, too brittle, too noisy, too hard to trust, or too poorly fit to the customer's real problem. * You care about the feeling of rightness. You notice naming, data models, logs, permissions, latency, API shape, loading behavior, failure modes, and the small frictions that make complex software feel either elegant or exhausting. * You use AI without outsourcing judgment. You can delegate work to agents, but you still define the problem, set review criteria, constrain the implementation, verify the behavior, and decide whether the result is good enough to ship. * You build with customers close by. You are comfortable using real customer workflows, support threads, calls, prototypes, and internal dogfooding to understand what the product needs to become. * You keep the team small by raising the bar. We would rather have a few engineers with strong taste, high agency, and deep ownership than a larger team producing more undifferentiated software. * You do not ship half-baked experiences to customers. Early prototypes are useful. Internal dogfooding is useful. Customer co-creation is useful. But the public product should feel cared for. What You'll Work On * Workflow and agent execution. Build the contracts that let agents call real pipelines with typed inputs, structured outputs, streaming status, retries, logs, and auditability. * Python and Django platform services. Model platform concepts cleanly, build durable APIs, evolve service boundaries, and make customer workflows reliable at the backend layer. * Kubernetes-backed execution. Work on deployment patterns, scaling, isolation, runtime behavior, observability, and operational reliability for AI workflows. * Fullstack platform surfaces. Build the frontend and backend experiences customers use to configure, run, inspect, debug, and reuse AI workflows. * Permission-aware data access. Build secure customer data access patterns, retrieval systems, indexing, hybrid search, and APIs that respect enterprise boundaries. * Reliability and observability. Make distributed AI workflows understandable: what ran, what changed, what failed, why it failed, and what the user or system can do next. * Platform abstractions. Turn one-off customer implementations into reusable capabilities without sanding off the important domain-specific details. * Developer and operator experience. Improve the tools, docs, test harnesses, internal workflows, and diagnostics that let a small team move quickly without losing control of the system. This is not a pure infrastructure role. The right person can move through the stack, find the real constraint, and leave the customer experience better shaped than they found it., * You have built workflow systems, developer platforms, data platforms, infrastructure products, agent systems, internal tools, or complex enterprise SaaS used by technical customers. * You have opinions about API shape, execution semantics, logs, permissions, lifecycle states, and observability because you have seen what happens when those things are treated as afterthoughts. * You can show how AI has made you faster without making your work worse. * You can point to places where you rejected, rewrote, constrained, or heavily edited generated code because your judgment was better than the tool's first answer. * You have pulled something back from release because it technically worked but did not yet meet the quality bar. * You are comfortable with ambiguity, but you do not confuse ambiguity with vagueness. You ask the questions that make the work concrete. * You care about regulated, high-trust AI because it is harder and more useful than another thin wrapper around a chat box., 3. Your GitHub, writing, technical design docs, shipped UI examples, or a representative sample of work you are proud of. 4. One example of a platform, workflow, developer tool, data product, or user-facing system you shipped where the architecture, abstraction, or customer experience judgment mattered. Tell us what you would do differently now. 5. One example of how you use AI in your engineering work. We are especially interested in where you overrode, constrained, rejected, or improved the AI's output. We will read everything. We will respond to everyone, including no's.