Note: The job is a remote job and is open to candidates in USA. Salt AI is building a governed execution platform for deployable, auditable, permission-aware, and reliable AI work, especially for regulated industries. The Senior Fullstack Platform Engineer will build backend- and platform-focused systems across workflow execution, agent orchestration, APIs, Kubernetes, data access, observability, permissions, and customer-facing fullstack product surfaces.
Responsibilities
- 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
Skills
- 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
- 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 engineering 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 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
- Fully Remote - Required to work during US-based time frames
Benefits
- Equity package
- 100% Employee Covered Medical, Dental, Vision Plan Base Plans (PPO & HMO)
- Life Insurance
- 401k
- Flexible Spending Accounts
- Fully Remote - Required to work during US-based time frames
Company Overview