Top Artificial Intelligence Software Engineer Jobs

Build and scale core AI-native healthcare products across the stack, focusing on real-time audio processing, NLU/transcription, clinical documentation, EHR integrations, and distributed production systems. Drive architecture, ship features with high ownership, and operate in a fast-moving, regulated environment.

Build and maintain a finance-grade, full-stack platform for trading and reserving AI compute. Own frontend and backend features, ensure correctness and reliability, shape performance and infrastructure, and contribute to deployment and operations.

As a Software Engineer, you will design, implement, and optimize software solutions, collaborate with teams, and support various stages of development while shaping company culture.

Build and maintain end-to-end agentic systems and retrieval pipelines to fetch, process, deduplicate, and surface large medical record sets. Work across frontend product and backend pipelines, apply LLMs and applied AI to improve reliability, match patients/facilities across messy networks, and ship customer-facing features while iterating with users.

The Software Engineer Intern will work with the founding team on full stack development, focusing on frontend UX, backend services, and AI features.

Lead and own full-stack projects across frontend, backend, and infrastructure. Build and ship features, collaborate with teammates, use creative problem solving, and contribute to company culture while working hybrid (3+ days/week).

Lead the unification of large heterogeneous datasets for generative audio models by creating robust systems for data ingestion, deduplication, and reconciliation. Collaborate with ML researchers and develop scalable entity-resolution solutions while tracking data quality metrics.

Seeking a Senior Lead/.NET Fullstack Developer to create scalable enterprise applications, focusing on exceptional user experiences and strong problem-solving skills.

Design behavioral specs, harnesses, and validation systems so AI agents implement reliable full-stack features. Build CI gates, structural tests, linting, knowledge bases, and end-to-end verification. Diagnose failures across spec, harness, and agent layers. Architect and operate production systems across cloud and on-prem environments for regulated energy deployments, prioritizing reliability, observability, and graceful degradation.

Lead GPU-edge inference systems, ensuring compliance and performance through design and monitoring. Manage global infrastructure for AI deployment.