Deskripsi Pekerjaan
At Nexus AI Labs, we are architecting the future of generative intelligence. We are looking for a Senior Machine Learning Engineer to join our core research and development team in San Francisco. This is a high-impact role where you will own the full lifecycle of large-scale foundation models—from data curation and distributed training to optimized inference and production deployment.
You will work alongside published researchers and world-class engineers to push the boundaries of what is possible with LLMs and multimodal systems. We offer a culture of radical ownership, compute abundance, and a commitment to open-source contribution.
Tanggung Jawab
- Design, train, and optimize large-scale deep learning models (LLMs, Diffusion, Multimodal) on massive GPU clusters (1000+ H100s/A100s).
- Build and maintain high-performance MLOps pipelines for data processing, training orchestration, evaluation, and CI/CD.
- Implement custom kernels and optimization techniques (FlashAttention, quantization, kernel fusion) using Triton/CUDA for maximum throughput.
- Collaborate with Product and Research to translate cutting-edge architectures into robust, low-latency production services.
- Lead the design of scalable model serving infrastructure (vLLM, TensorRT-LLM, Triton Inference Server) supporting millions of daily requests.
- Mentor junior engineers and establish best practices for reproducibility, experiment tracking, and model governance.
- Contribute to open-source AI ecosystem and publish findings at top-tier conferences (NeurIPS, ICML, ICLR).
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, or equivalent practical experience (5+ years industry ML experience).
- Expert-level proficiency in Python and deep learning frameworks (PyTorch, JAX, TensorFlow).
- Proven track record of training and deploying Large Language Models (7B+ parameters) or large-scale vision models.
- Deep understanding of distributed training paradigms (FSDP, DeepSpeed, Megatron-LM, 3D Parallelism).
- Strong systems engineering skills: Linux internals, networking (RDMA/InfiniBand), GPU architecture, and containerization (Docker, Kubernetes).
- Experience with MLOps tools (MLflow, Weights & Biases, Kubeflow, Airflow) and feature stores.
- Publications in top-tier venues or significant open-source contributions (e.g., Hugging Face, vLLM, PyTorch Lightning).