🚀 We're Hiring :- Member of Technical Staff – Model Training

💼 Job Type :- Full-time
🌍 Work Mode :- Remote
💰 Compensation :- ₹2 Cr – ₹3 Cr per annum

🧠 About the Role

Are you ready to build the infrastructure powering the next generation of AI? 🚀

We are looking for an exceptional Member of Technical Staff – Model Training (Infrastructure & Platform) to architect and scale high-performance distributed systems that accelerate AI research, model training and intelligent agent execution.

In this role, you will take technical ownership of mission-critical infrastructure spanning distributed machine learning, GPU computing, model inference, reinforcement learning, simulation environments, and AI agent execution frameworks. ⚙️ Your work will directly influence training efficiency, research velocity, and the performance of increasingly capable AI systems.

If you thrive at the intersection of systems engineering, high-performance computing, and cutting-edge AI, this is an opportunity to solve challenging problems with significant technical impact. 💡

🛠️ Key Responsibilities

• 🏗️ Architect Distributed Systems :- Design, build, and scale infrastructure for supervised fine-tuning (SFT), reinforcement learning (RL), and on-policy distillation (OPD) across language, multimodal, and robotics workloads.

• 🔄 Build Scalable Training Pipelines :- Develop distributed execution systems coordinating GPU workers, model inference, rollout generation, reward computation, and simulation environments.

• ⚡ Optimize GPU Performance :- Improve GPU utilization, memory efficiency, inter-process communication, storage performance, scheduling, and distributed execution.

• 🤖 Develop AI Agent Infrastructure :- Build and evolve execution runtimes, tool interfaces, environment integrations, model registries, and model-serving APIs.

• 🛡️ Strengthen Infrastructure Reliability :- Implement experiment tracking, dataset and model versioning, checkpointing, fault recovery, and observability systems.

• 📈 Eliminate Performance Bottlenecks :- Identify infrastructure limitations and reduce training time, experiment turnaround, and operational overhead.

• 🧩 Drive Technical Architecture :- Establish platform interfaces, engineering standards, and scalable architectural patterns across research and engineering teams.

• 👨‍💻 Lead Engineering Initiatives :- Own complex projects from architecture and implementation to deployment and production operations while mentoring engineers.

🎯 Required Qualifications

• 🧠 Strong computer science fundamentals in distributed systems, concurrency, networking, algorithms, and performance engineering.

• 🏢 Extensive experience designing and operating scalable backend systems, distributed computing platforms, or high-performance infrastructure.

• 💻 Strong proficiency in Python and at least one systems programming language, such as C++, Rust, or Go.

• 🖥️ Hands-on experience with GPU-based machine learning infrastructure, distributed model training, inference optimization, or large-scale compute orchestration.

• ⚙️ Deep understanding of system performance, including compute utilization, memory management, communication overhead, scheduling, and fault tolerance.

• 🔌 Experience designing reliable APIs, backend services, and infrastructure components for complex engineering or research workflows.

• 🏆 Demonstrated technical leadership and ownership of architecture, cross-functional initiatives, and production-critical systems.

• 🚀 Ability to work effectively in fast-paced AI research environments where infrastructure must evolve rapidly to support experimentation.

🌟 Preferred Qualifications

• 🔬 Experience with distributed ML frameworks such as PyTorch Distributed, DeepSpeed, Ray, or Megatron-LM.

• ⚡ Familiarity with inference engines and optimization frameworks such as vLLM or TensorRT-LLM.

• 🎮 Experience building reinforcement learning infrastructure, rollout orchestration, or distributed agent execution systems.

• 🔧 Knowledge of CUDA, GPU kernel optimization, NCCL, and low-level GPU performance engineering.

• ☁️ Experience with Kubernetes, container orchestration, cluster scheduling, and cloud-based GPU infrastructure.

• 🤖 Familiarity with robotics infrastructure, simulation platforms, multimodal AI, or large-scale agent evaluation.

• 🌐 Contributions to open-source distributed computing frameworks, machine learning infrastructure, or high-performance systems.

💎 Why Consider This Opportunity?

• 💰 Exceptional Compensation :- Annual compensation of ₹2 Cr – ₹3 Cr.

• 🚀 High-Impact Engineering :- Build foundational infrastructure that enables advanced AI research and model development.

• 🧭 Technical Ownership :- Influence core architectural decisions and lead mission-critical engineering initiatives.

• ⚙️ Complex Systems Challenges :- Work across distributed computing, GPU infrastructure, reinforcement learning, and AI agent platforms.

• 📊 Research at Scale :- Enable faster experimentation, efficient model training, and reliable deployment of advanced AI systems.

• 🌍 Full-Time Remote Opportunity :- Collaborate across research and engineering workflows in a remote work environment.