Staff Machine Learning Operations Engineer, SandboxAQ

• You’ll bring broad experience with infrastructure for automated training, evaluation, and maintenance of trained models; systems for efficient inference on trained models; and practices for versioning dataset and models
• Ideal candidates will bring a blend of experience with highly-automated, stable MLOps pipelines for highly-available systems, and scrappy ad-hoc pipelines for R&D projects
• Most importantly, you’ll bring a track record of working in a fast-moving software development team, exploring new technologies, and solving problems across an entire software stack
• 7+ years of experience with MLOps fundamentals:
• Automated training, evaluation, and retraining loops
• Dataset and model versioning tools, Weights & Biases preferred
• Systems for serving inference
• Build end-to-end ML pipeline using industry-standard tools
• Deep experience with at least one major cloud provider. GCP preferred
• Experience with managing complex data governance requirements
• Familiarity with MLOps architectures and best practices for LLMs and Agentic systems
• 3+ years of experience with infrastructure as code management of public cloud providers. Familiar with terraform. GCP preferred
• Familiarity with building and maintaining CI/CD pipelines for ML systems
• 3+ years of experience with Python, with strong knowledge of software design principles
• Familiarity with building data pipelines or data processing systems at scale, including orchestration tools like Airflow
• Excellent communication and collaboration skills, with the ability to effectively influence a cross-functional team

Desirable

• SandboxAQ is seeking a Staff MLOps engineer to mature our MLOps practices by building out infrastructure and application code, as well as embedding with R&D teams to champion adoption of your tools and gather feedback to improve them
• In this role, you’ll be working closely with data engineers, software developers, research scientists, and ML scientists to deliver solutions at the frontier of AI in chemistry and the life sciences
• This is a hands-on-keyboard role, however it includes the opportunity to build and lead a team of MLOps engineers for interested candidates
• Mature our MLOps practice by defining processes and building fundamental tooling
• Embed closely with R&D teams to assist in delivering project goals and drive adoption of practices and tooling
• Drive the design and implementation of complex, security-sensitive data processing and storage systems with complex tenancy and data isolation requirements
• Collaborate closely with the product team and internal stakeholders in all phases of software development to validate the solutions you propose and implement
• In collaboration with the rest of the engineering team, build and manage infrastructure for SandboxAQ’s simulation and data platform
• Review code and participate in design and architectural discussions