Senior Applied ML Engineer – ML4Sys

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Company Overview

A leading data and AI technology organization is seeking an experienced Senior Applied ML Engineer – ML4Sys to join its Applied AI team. The organization develops cutting-edge machine learning solutions that improve the efficiency, scalability, and performance of large-scale cloud infrastructure and distributed systems. This role offers the opportunity to build advanced AI-driven systems that directly impact enterprise customers through optimized compute performance and cost efficiency.

The organization is seeking a Senior Applied ML Engineer – ML4Sys to develop and deploy advanced machine learning, scheduling, and optimization algorithms that maximize infrastructure performance and efficiency. The successful candidate will work across the technology stack, from cluster management to query compilation, solving complex engineering challenges while delivering scalable and cost-effective workloads.

This position requires a strong background in machine learning, distributed systems, cloud infrastructure, and software engineering. The ideal candidate will collaborate with cross-functional engineering and product teams to build production-grade ML systems, shape long-term AI strategy, and drive innovation in large-scale computing environments.

Key Responsibilities

• Accelerate the growth and efficiency of serverless compute products through advanced optimization techniques.
• Design and build end-to-end ML4Sys solutions from the ground up within a collaborative team of domain experts.
• Define the roadmap for applied machine learning initiatives in partnership with engineering and product leadership.
• Architect, train, deploy, and maintain state-of-the-art machine learning models that improve product performance and operational efficiency.
• Develop scalable machine learning pipelines, data processing frameworks, model serving infrastructure, and production monitoring systems.
• Research and implement innovative modeling techniques tailored for computer systems and distributed computing environments.
• Optimize large-scale cloud infrastructure through data-driven machine learning solutions.
• Collaborate across engineering teams to deliver highly reliable and scalable AI-powered infrastructure.

Minimum Qualifications

• Background in Computer Science with a Master’s degree in Machine Learning, Data Science, or a related computational discipline such as Artificial Intelligence, Bioinformatics, Electrical Engineering, or Physics.
• Strong experience building, training, deploying, and maintaining machine learning models in production environments.
• Practical knowledge of cloud computing, distributed systems, and modern data processing frameworks.
• Proficiency in Python, Scala, or Java.

Preferred Qualifications

• PhD in Artificial Intelligence, Data Science, or a related technical discipline.
• 4+ years of machine learning engineering experience in a high-growth, fast-paced environment.
• Strong understanding of computer architecture, distributed computing, cloud infrastructure, database internals, or networking.
• Experience with operations research, forecasting, Markov Decision Processes, or other optimization algorithms for sequential decision-making.
• Demonstrated success optimizing large-scale distributed systems or cloud infrastructure using data-driven methodologies.

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