Deskripsi Pekerjaan
Shape the Future of Generative AI
At NexGen AI Labs, we are building the foundational models that power the next generation of enterprise intelligence. We are seeking a Senior Machine Learning Engineer to join our core research team in the heart of San Francisco. This is a high-autonomy, high-impact role where you will architect, train, and deploy large-scale models solving complex reasoning and multimodal understanding problems.
You will work alongside published researchers and world-class engineers in a culture that values intellectual honesty, rigorous experimentation, and rapid iteration. We offer a competitive equity package, top-tier healthcare, and a compute budget that removes barriers to innovation.
Why This Role Is Different
- Ownership: Lead end-to-end model lifecycle from data curation to production monitoring.
- Scale: Access to massive GPU clusters (H100/A100) for training foundation models.
- Impact: Your work directly ships to Fortune 500 clients and open-source community.
Tanggung Jawab
- Design, implement, and optimize distributed training pipelines for Large Language Models (LLMs) and Multimodal architectures using PyTorch, JAX, or TensorFlow.
- Conduct novel research in areas such as RLHF/RLAIF, model compression (quantization/distillation), and efficient attention mechanisms.
- Build and maintain robust MLOps infrastructure: feature stores, experiment tracking, model registry, and automated evaluation harnesses.
- Collaborate with Product and Data teams to define data strategies, curate high-quality pre-training/instruction datasets, and establish evaluation benchmarks.
- Optimize inference latency and throughput for production serving (TensorRT-LLM, vLLM, Triton Inference Server) on Kubernetes clusters.
- Mentor junior engineers, conduct code reviews, and establish best practices for reproducible ML research.
- Publish findings at top-tier conferences (NeurIPS, ICML, ICLR) and contribute to open-source tooling.
Kualifikasi
- M.Sc. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience (5+ years industry ML experience).
- Proven track record of training and deploying large-scale models (>1B parameters) in production environments.
- Deep expertise in distributed training frameworks (FSDP, DeepSpeed, Megatron-LM) and GPU optimization (CUDA kernels, NCCL).
- Strong software engineering fundamentals: Python, C++, Rust, or Go; proficiency with Linux, Docker, Kubernetes, and CI/CD.
- Solid theoretical grounding in optimization, probability, and deep learning architectures (Transformers, Diffusion, SSMs).
- Experience with LLM alignment techniques (SFT, DPO, PPO, Constitutional AI) and evaluation methodologies.
- Excellent communication skills; ability to translate complex research concepts to cross-functional stakeholders.