DeepAware AI (YC S25)
About the Role \ DeepAware AI (YC S25) is building secure, efficient, and autonomous infrastructure for the AI era. As an AI/ML Engineer, you’ll design, build, and deploy machine learning models that power our next-generation Data Center Infrastructure Management (DCIM) platform. Your work will focus on
reinforcement learning
for intelligent workload scheduling,
optimization algorithms
for energy and cost savings, and
anomaly detection
to prevent downtime and enhance security.
You’ll be joining a fast-moving, technically ambitious team tackling some of the most complex real-world AI problems at the intersection of computing, energy, and robotics.
Responsibilities
Develop and refine reinforcement learning models for GPU workload placement and power optimization Implement anomaly detection pipelines for real-time threat detection and failure alerts Collaborate with data engineers to ensure high-quality, production-ready datasets Benchmark models against industry baselines and integrate them into our production systems Contribute to overall architecture and deployment strategies for large-scale AI infrastructure
Requirements
Strong background in machine learning; hands-on experience with reinforcement learning techniques Proficiency in Python and PyTorch or TensorFlow Experience with distributed training and deployment in production environments Familiarity with energy systems, scheduling algorithms, or operations research is a plus Ability to thrive in a startup environment — ownership mindset, adaptability, and collaborative spirit
Nice-to-Have
Experience with NVIDIA CUDA/cuDNN, Triton Inference Server, or ROS2 for robotics integration Knowledge of data center operations or AI infrastructure optimization
Location: \ San Francisco Bay Area preferred; remote considered for exceptional candidates
Why DeepAware? \ You’ll be working on problems that directly impact the sustainability and reliability of the world’s AI infrastructure — with a team that values technical excellence, creativity, and impact. #J-18808-Ljbffr
reinforcement learning
for intelligent workload scheduling,
optimization algorithms
for energy and cost savings, and
anomaly detection
to prevent downtime and enhance security.
You’ll be joining a fast-moving, technically ambitious team tackling some of the most complex real-world AI problems at the intersection of computing, energy, and robotics.
Responsibilities
Develop and refine reinforcement learning models for GPU workload placement and power optimization Implement anomaly detection pipelines for real-time threat detection and failure alerts Collaborate with data engineers to ensure high-quality, production-ready datasets Benchmark models against industry baselines and integrate them into our production systems Contribute to overall architecture and deployment strategies for large-scale AI infrastructure
Requirements
Strong background in machine learning; hands-on experience with reinforcement learning techniques Proficiency in Python and PyTorch or TensorFlow Experience with distributed training and deployment in production environments Familiarity with energy systems, scheduling algorithms, or operations research is a plus Ability to thrive in a startup environment — ownership mindset, adaptability, and collaborative spirit
Nice-to-Have
Experience with NVIDIA CUDA/cuDNN, Triton Inference Server, or ROS2 for robotics integration Knowledge of data center operations or AI infrastructure optimization
Location: \ San Francisco Bay Area preferred; remote considered for exceptional candidates
Why DeepAware? \ You’ll be working on problems that directly impact the sustainability and reliability of the world’s AI infrastructure — with a team that values technical excellence, creativity, and impact. #J-18808-Ljbffr