Top Remote Artificial Intelligence Software Engineer Jobs

The role involves designing data ingestion and processing systems, writing scalable backend applications, and optimizing performance of business-critical endpoints.

Build systems integrated with clinical workflows, simplify healthcare complexities, and ensure quality and trust in software. Collaborate effectively in a team-oriented environment.

Develop and scale backend systems for simulation environments, focusing on high-performance Python applications, multi-threading, and collaboration with robotics teams. Responsibilities include system design, performance optimization, mentoring, and developing SDKs.

As a Senior Software Engineer focusing on Agentic Systems, you will develop new features for our Conversational Video Interface and enhance human-computer interactions. You will also work across the product stack while ensuring system reliability and observability.

The Senior Backend Engineer will develop core functionalities for AI-driven drug discovery, collaborating with R&D and product teams, managing infrastructure, and ensuring engineering excellence.

The VP of Engineering will lead the engineering team, manage resources, drive architectural decisions, and collaborate on the technical roadmap while ensuring system reliability and performance.

As a Solutions Engineer, you'll design and implement social intelligence solutions for clients, translating AI capabilities into business value and collaborating across functions to ensure client success.

Design and maintain geometry processing algorithms, develop services for 2D/3D data, and optimize post-training workflows for machine learning models.

The Applied AI Engineer will develop end-to-end proofs-of-concept for AI integration, prototype systems rapidly, and collaborate with research and backend teams to deliver functional demos and documentation.

As a Staff Software Engineer, you will lead the development of Cake's AI platform infrastructure, focusing on building, operating, and improving ML systems. The role requires collaboration with customers to address ML challenges and enhance platform capabilities, emphasizing ownership and operational excellence.