Staff Machine Learning Operations Engineer

Summary

Join SandboxAQ, a high-growth company delivering AI solutions, as an AI/ML infrastructure architect. You will design and build MLOps pipelines for next-generation health technologies, working with research and product engineering teams. This role requires strong technical leadership, making architectural recommendations and guiding the AI/DS team. You will implement robust MLOps pipelines on AWS, train models at scale, and implement data governance systems. The ideal candidate possesses extensive experience in AWS, MLOps, and AI/ML, thrives in a startup environment, and excels in communication and collaboration. SandboxAQ offers competitive salaries, stock options, generous learning opportunities, comprehensive benefits, and a supportive, inclusive work environment.

Requirements

• You understand Deep Learning and Machine Learning well enough to help deploy, maintain, and monitor models in production environments
• You have 7+ years of experience in AWS, MLOps, and AI/ML, which allows you to make skillful recommendations about which services would be best to use for a given AI Product
• You thrive in a startup environment and are able to balance the need for speed with the desire to build solid, reliable software
• You know how to listen and communicate respectfully with key stakeholders to understand their requirements and collaboratively work through any mutual concerns, before finalizing an architectural decision
• You are a “doer” with communication skills; you know how to ask the right questions and how to talk to the key stakeholders, but once the product goals are clear, you implement rapidly and skillfully
• You love to learn about the latest evolutions in AI/ML, and you get a deep intrinsic reward from finally deploying these cutting-edge AI/ML models and monitoring their impact

Responsibilities

• Make clear, well-researched, and experience-based architectural recommendations, which support the delivery of complex AI SAAS products to customers
• Analyze and communicate the critical trade-offs in competing architectural options, by articulating the impact on the product quality and scalability, technical complexity, timeline for delivery, and ongoing maintenance requirements
• Guide the AI/DS team toward continual improvement in fundamental engineering best practices
• Build complex yet robust MLOps pipelines to support the delivery of AI SAAS products
• Provide experienced recommendations for the best AWS services to utilize for optimal MLOps pipeline delivery
• Implement systems to train existing Dl and ML models at scale, and iterate rapidly toward new AI/ML products
• Implement tools and methodologies that support solid data governance, including monitoring of traceability, data quality, data security, etcetera
• Conduct code reviews for more junior AI/ML scientists

Benefits

• Annual discretionary bonuses
• Equity
• Competitive salaries
• Stock options
• Generous learning opportunities
• Medical/dental/vision
• Family planning/fertility
• PTO (summer and winter breaks)
• Financial wellness resources
• 401(k) plans