Sr. Fullstack Product Engineer - (Frontend Focus)

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 visual polish.

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 ugly, too hard to trust, or too poorly fit to the customer’s real problem.
• You care about the feeling of rightness. You notice naming, hierarchy, density, empty states, logs, permissions, latency, copy, transitions, 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.

• High-craft product experiences. Create beautiful, deeply usable interfaces for complex workflows: design systems, motion, dense information displays, stateful tools, and interaction patterns customers can trust.
• Agentic customer surfaces. Build experiences where users can run, guide, inspect, approve, and recover AI workflows without losing trust in what the system is doing.
• Human-in-the-loop systems. Create interaction patterns for approvals, parameter review, result inspection, exception handling, and other moments where users need to make load-bearing decisions.
• Workflow and execution UI. Make distributed AI workflows understandable: what ran, what changed, what failed, why it failed, and what the user or system can do next.
• Design systems and product coherence. Help Salt feel like one product instead of disconnected surfaces. Build reusable components, interaction patterns, and state models that scale.
• Fullstack product surfaces. Work across React, TypeScript, APIs, backend services, and platform contracts to configure, run, inspect, debug, and reuse AI workflows.
• Retrieval and customer data experiences. Build interfaces for permission-aware search, indexed customer data, result inspection, citations, confidence, and trust.
• Platform abstraction through UX. Turn complex backend capabilities into product primitives customers and internal teams can safely build on.

This is not a pure frontend role.

The right person can move through the stack, find the real constraint, and leave the customer experience better shaped than they found it.

• 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.

• 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.

Being direct about this saves everyone time.

• You want a narrow frontend-only, design-only, backend-only, ML-only, or infrastructure-only role.
• You need detailed tickets before you can make progress.
• You are looking for a large team with mature process, fixed swimlanes, and lots of scaffolding.
• You are comfortable shipping AI-generated code you do not fully understand.
• You think “AI-native” means letting an agent produce thousands of lines of code and asking reviewers to find the problems later.
• You see backend architecture, reliability, product judgment, design, or customer context as someone else’s job.
• You ship something once and move on before it has proven itself in production.

• Small team, high ownership. We are early enough that a strong engineer can meaningfully change the shape of the product.
• AI tooling is part of the job. We expect high leverage from modern engineering tools, and we expect the judgment to keep that leverage from turning into drift. Everyone here uses AI; the differentiator is whether it makes your work better.
• Customer reality matters. You will be close to real customer problems, especially in life sciences and other regulated environments.
• Cross-functional by default. Product, design, engineering, customer feedback, and delivery are tightly connected here. Engineers are expected to think, not just execute.
• Low bureaucracy. We value clear writing, direct conversation, working software, and people who make the system easier for others to reason about.

• Competitive salary and equity package
• 100% Employee Covered Medical, Dental, Vision Plan Base Plans (PPO & HMO)
• Life Insurance, 401k, Flexible Spending Accounts, & More
• Fully Remote - Required to work during US-based time frames

Send us:

• A short note about why this role specifically. Not a generic cover letter.
• A description of where you are strongest across frontend/product experience and where you still feel comfortable across the rest of the stack.
• Your GitHub, portfolio, writing, shipped UI examples, technical design docs, or a representative sample of work you are proud of.
• 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.
• 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.

Based in Southern California, Salt AI is pioneering the future of life sciences with advanced AI.

Founded in 2024 by Aber Whitcomb and Jim Benedetto—veterans of MySpace, Jam City, Gravity, and Core Scientific—our leadership team brings over 18 years of collaboration.

We’re not just building products, but transforming what’s possible in research and discovery.

We value diverse perspectives and are committed to an inclusive team.

If you’re excited about shaping the future of AI in life sciences and beyond, we'd love to connect with you.