Machine Learning Engineer - Inference

📋 Description

• • Design and build user-facing APIs, SDKs, and tools that enable researchers, developers, and enterprise clients to access Mindbeam’s cutting-edge ML infrastructure.
• • Translate complex machine learning workflows from research teams into intuitive, usable abstractions that simplify adoption for non-specialist users.
• • Optimize all interfaces for scalability, security, and high performance under enterprise-grade workloads.
• • Advocate for developer experience by actively gathering feedback from users and iterating rapidly on tooling and documentation.
• • Collaborate cross-functionally with research, product, and engineering teams to ensure seamless integration of ML systems into enterprise environments.
• • Develop and maintain robust, well-documented interfaces that support secure, compliant deployment in regulated industries.
• • Implement monitoring and logging capabilities within APIs and SDKs to enable observability and rapid troubleshooting in production.
• • Stay current with advancements in ML inference optimization, model serving, and developer tooling to continuously improve Mindbeam’s offerings.
• • Ensure all tools adhere to industry best practices for authentication, authorization, data privacy, and API versioning.
• • Participate in code reviews, architecture discussions, and technical planning sessions to align tooling with broader infrastructure goals.
• • Contribute to internal and external documentation, tutorials, and examples to lower the barrier to entry for new users.
• • Identify performance bottlenecks in inference pipelines and propose architectural improvements to reduce latency and increase throughput.
• • Work with cloud platforms and distributed systems to deploy and scale ML inference services reliably across diverse environments.
• • Engage with open source communities to contribute improvements and gather insights from external developers using Mindbeam’s tools.
• • Balance innovation with stability, ensuring new features are thoroughly tested before release to enterprise customers.
• • Prioritize accessibility in tool design to support users with varying levels of ML expertise, from academic researchers to enterprise IT teams.
• • Maintain a strong focus on usability, ensuring interfaces are intuitive, consistent, and reduce cognitive load for end users.
• • Represent Mindbeam’s engineering team in customer-facing technical discussions to understand pain points and translate them into product improvements.