PythonReview Criteria:
Mandatory:
- Strong AI Runtime Engineering (Lead / Staff) Profiles
- Must have 4+ years of software engineering experience
- Must have proven 1+ years of experience designing, building, and owning AI runtime infrastructure supporting distributed training and/or inference at scale
- Must have hands-on experience optimizing deep learning runtimes such as PyTorch, TensorFlow, etc
- Must have strong low-level performance engineering experience, including profiling, debugging, and optimizing system throughput, latency, and reliability
- Must have experience leading or mentoring a team, including technical guidance, code reviews, and delivery ownership
- Must have strong programming skills in Python, Java, C++ , etc
Preferred:
Experience with Kubernetes, Ray, TorchElastic, or custom AI job orchestration frameworks
Exposure to LLM training pipelines, checkpointing, elastic or distributed training orchestration
Role & Responsibilities:
As Lead/Staff AI Runtime Engineer, you’ll play a pivotal role in the design, development, and optimization of the core runtime infrastructure that powers distributed training and deployment of large AI models (LLMs and beyond). This is a hands-on leadership role - perfect for a systems-minded software engineer who thrives at the intersection of AI workloads, runtimes, and performance-critical infrastructure. You’ll own critical components of our PyTorch-based stack, lead technical direction, and collaborate across engineering, research, and product to push the boundaries of elastic, fault-tolerant, high-performance model execution.
What you’ll do:
Lead Runtime Design & Development:
- Own the core runtime architecture supporting AI training and inference at scale.
- Design resilient and elastic runtime features (e.g. dynamic node scaling, job recovery) within our custom PyTorch stack.
- Optimize distributed training reliability, orchestration, and job-level fault tolerance.
Drive Performance at Scale:
- Profile and enhance low-level system performance across training and inference pipelines.
- Improve packaging, deployment, and integration of customer models in production environments.
- Ensure consistent throughput, latency, and reliability metrics across multi-node, multi- GPU setups.
Build Internal Tooling & Frameworks:
- Design and maintain libraries and services that support model lifecycle: training, check pointing, fault recovery, packaging, and deployment.
- Implement observability hooks, diagnostics, and resilience mechanisms for deep learning workloads.
- Champion best practices in CI/CD, testing, and software quality across the AI Runtime stack.
Collaborate & Mentor:
- Work cross-functionally with Research, Infrastructure, and Product teams to align runtime development with customer and platform needs.
- Guide technical discussions, mentor junior engineers, and help scale the AI Runtime team’s capabilities.
Ideal Candidate:
- 5+ years of experience in systems/software engineering, with deep exposure to AI runtime, distributed systems, or compiler/runtime interaction.
- Experience in delivering PaaS services.
- Proven experience optimizing and scaling deep learning runtimes (e.g. PyTorch, TensorFlow, JAX) for large-scale training and/or inference.
- Strong programming skills in Python and C++ (Go or Rust is a plus).
- Familiarity with distributed training frameworks, low-level performance tuning, and resource orchestration.
- Experience working with multi-GPU, multi-node, or cloud-native AI workloads.
- Solid understanding of containerized workloads, job scheduling, and failure recovery inproduction environments.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon