Machine Learning Infrastructure Engineer

Kadence Talent is excited to partner with an innovative AI and Quantum start-up seeking a skilled Machine Learning Infrastructure Engineer specializing in GPU optimization. This role is essential to empower researchers by facilitating seamless handling of compute-intensive workloads on a large scale.

As a pivotal member of our team, you will bridge the gap between machine learning and infrastructure, ensuring that scientists can effortlessly transition from localized experiments to GPU-enhanced systems across diverse cloud platforms.

What You'll Do:

• Enable scalable access to GPUs across AWS and GCP environments.
• Design and implement straightforward systems for managing compute-heavy workloads.
• Enhance the speed, reliability, and cost-effectiveness of experimental processes.
• Assist in transitioning workflows from Modal native cloud.
• Support ongoing research initiatives using advanced tools such as Qiskit and PennyLane.
• Minimize complexities to create an invisible and user-friendly infrastructure.

What We're Looking For:

• Proficient experience with AWS or GCP, focusing on computing and basic networking.
• Familiarity with GPU workloads, particularly with tools like PyTorch and CUDA.
• Strong proficiency in Python programming.
• Experience with Docker; knowledge of Kubernetes is a plus.
• Adept at working within a dynamic startup environment.

Nice to Have:

• Experience with ML/AI infrastructure or training pipelines.
• Knowledge of distributed computation frameworks (Ray, Dask, Spark, etc.).
• Background in supporting researchers or data scientists.

Why This Role:

• Join us at the cutting edge of quantum technology, machine learning, and infrastructure.
• Have the opportunity to create impactful systems from the ground up - enjoy high ownership and significant influence.
• Transform underutilized computing resources into meaningful research outcomes.

Location:

This is an on-site role in Mountain View, CA

Compensation:

$250k - $280k+, plus equity