Senior Fullstack Platform Engineer – Backend Focus

🕒 il y a 3 mois

🏄 California – Distant

🏄 California – Remote

💵 $14 000 000 - $18 000 000 / an

⏰ Temps Plein

🟠 Senior

🔙 Ingénieur Back-end

👻 Score fantôme 30%

Ce que ça veut dire : la probabilité que personne ne recrute vraiment pour cette offre.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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Salt AI

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11 - 50 employés

Fondée en 2024

🏢 Entreprise

📋 Conformité

🏥 Santé

🔥 Financement dans la dernière année

💰 €10 000 000 Seed Round - Salt AI en 2025-09

Enterprise • Compliance • Healthcare

Salt AI est un fournisseur d'infrastructure d'intelligence artificielle contextuelle et d'orchestration conçu pour les entreprises réglementées. Sa plateforme Salt OS unifie les modèles, les données, la gouvernance et le contexte dans un système unique, déployable qui fonctionne là où se trouvent les données des clients (VPC/sur site), est modèle-et cloud-agnostique, et applique par défaut l'auditabilité et la conformité. Salt AI met l'accent sur l'absence d'accès public, la traçabilité immuable et les pistes d'audit, ainsi que sur les contrôles pré-validés (SOC 2 et santé) pour soutenir les cas d'utilisation à haute conformité. L'entreprise cible les secteurs réglementés tels que les sciences de la vie, les services financiers, l'énergie et les services publics, et le gouvernement, permettant des cas d'utilisation tels que la conception moléculaire, les pipelines cliniques, les pipelines de modèles de risque, les rapports réglementaires et l'optimisation opérationnelle.

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.

🎯 Exigences

• 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.

🏖️ Avantages

• 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

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