Sr. Fullstack Product Engineer - (Frontend Focus)

Salt Ai•Los Angeles, CA

9h•Remote

About The Position

Salt AI is building the governed execution platform for AI work that has to be real: deployable, auditable, permission-aware, and reliable enough for regulated industries. Our customers are doing work where “the demo looked cool” is not enough. In life sciences, that can mean RNA-targeted drug discovery, combinatorial chemistry, clinical-trial-adjacent research, and scientific workflows that need traceability, reproducibility, and trust. In financial services, healthcare, legal, and government, it means sensitive data, private infrastructure, and AI systems that have to be controlled rather than merely impressive. We are looking for a Senior Full Stack Product Engineer who can help turn that platform into something customers can actually build on: software that feels considered, powerful, fast, trustworthy, and unusually good for the complexity underneath. This role leans frontend and customer experience. You should go deep on React, TypeScript, design systems, motion, interaction design, dense information interfaces, and the craft of making complex software feel powerful, calm, and beautiful. But the scope is still fullstack. You are not just implementing screens. You are responsible for the full stack of the customer experience: the interface, the interaction details, the runtime behavior, the APIs, the data contracts, the failure states, and the quality bar of the thing a customer actually uses.

Requirements

Strong frontend and product craft. You are credible in React, TypeScript, interaction design, design systems, animation, state management, responsive UI, and customer-facing product quality.
Fullstack production range. You are comfortable enough with Python/Django, APIs, backend services, data models, jobs, and platform constraints to shape the systems behind the interface, not merely consume them.
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.
Product and design taste. You can tell when a complex workflow is technically correct but still confusing, brittle, ugly, or hard to trust. You care about making advanced software feel legible, fast, and empowering.
Platform instincts. You think in contracts, interfaces, failure modes, permissions, observability, and lifecycle. You know how backend decisions shape the user’s experience.
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.
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.
Judgment in spite of AI. You can deliver high-quality software even when AI tools are eager to generate too much code, plausible abstractions, brittle tests, or shallow solutions.
Customer empathy. You can talk to scientists, data teams, operators, and enterprise stakeholders, then translate messy real-world needs into durable product decisions.
Clear communication. You can write down the shape of a problem, explain tradeoffs, and help the team make better decisions without turning everything into a meeting.

Nice To Haves

You have built design-heavy enterprise SaaS, workflow tools, developer tools, data products, internal tools, agent systems, or complex customer-facing products used by technical customers.
You like the space between product UX and backend architecture.
You have opinions about UI states, motion, density, information hierarchy, API shape, execution semantics, logs, and permissions 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.

Responsibilities

Create beautiful, deeply usable interfaces for complex workflows: design systems, motion, dense information displays, stateful tools, and interaction patterns customers can trust.
Build experiences where users can run, guide, inspect, approve, and recover AI workflows without losing trust in what the system is doing.
Create interaction patterns for approvals, parameter review, result inspection, exception handling, and other moments where users need to make load-bearing decisions.
Make distributed AI workflows understandable: what ran, what changed, what failed, why it failed, and what the user or system can do next.
Help Salt feel like one product instead of disconnected surfaces. Build reusable components, interaction patterns, and state models that scale.
Work across React, TypeScript, APIs, backend services, and platform contracts to configure, run, inspect, debug, and reuse AI workflows.
Build interfaces for permission-aware search, indexed customer data, result inspection, citations, confidence, and trust.
Turn complex backend capabilities into product primitives customers and internal teams can safely build on.
Work across the platform layer and the application layer: workflow execution, agent orchestration, pipeline-as-tool contracts, customer data access, retrieval, permissions, observability, and the UI surfaces that expose those capabilities to real users.
Shape the API and event contract behind the interface.
Dig through backend behavior to understand why the customer experience does not feel right yet.
Build product surfaces that make Salt more trustworthy, more composable, easier for customers to understand, and dramatically better to use.
Use AI to increase your leverage while keeping your standards intact.
Keep the team small by raising the bar.
Build with customers close by.
Use AI without outsourcing judgment.