Senior Fullstack Platform Engineer – Backend Focus

🔥 25 minutes ago

🏄 California – Remote

🏄 California – Remote

💵 $14M - $18M / year

⏰ Full Time

🟠 Senior

🔙 Backend Engineer

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Salt AI

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2024

🏢 Enterprise

📋 Compliance

🏥 Healthcare

🔥 Funding within the last year

💰 $10M Seed Round - Salt AI on 2025-09

Enterprise • Compliance • Healthcare

Salt AI is a provider of contextual AI infrastructure and orchestration designed for regulated enterprises. Its Salt OS platform unifies models, data, governance, and context into a single, deployable system that runs where customers’ data lives (VPC/on-prem), is model- and cloud-agnostic, and enforces auditability and compliance by default. Salt AI emphasizes no public egress, immutable lineage and audit trails, and pre-validated controls (SOC 2 and healthcare) to support high-compliance use cases. The company targets regulated sectors including life sciences, financial services, energy & utilities, and government, enabling use cases such as molecular design, clinical pipelines, risk model pipelines, regulatory reporting, and operational optimization.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ Benefits

• 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