Deskripsi Pekerjaan
At Vertex AI Labs, we are building the foundational infrastructure for the next generation of Generative AI applications. We are looking for a Senior Machine Learning Engineer to lead the design, training, and deployment of large-scale foundation models. This is a high-impact role where you will own the full ML lifecycle—from data curation and distributed training optimization to production serving infrastructure.
You will work alongside a world-class team of researchers and engineers published at NeurIPS, ICML, and ICLR. We value deep technical rigor, speed of iteration, and a product-first mindset. We offer a hybrid work model (3 days onsite in our SoMa office) with flexible hours and a significant equity package.
Why Join Us?
- Frontier Tech: Work on 70B+ parameter models and cutting-edge RAG architectures.
- Ownership: End-to-end responsibility for model performance in production.
- Compensation: Top-of-market salary, generous equity (ISO grants), and 401k matching.
- Growth: Dedicated research budget, conference sponsorships, and mentorship from industry veterans.
Tanggung Jawab
- Design and implement scalable training pipelines for LLMs and multimodal models using PyTorch/JAX and distributed frameworks (FSDP, DeepSpeed, Megatron-LM).
- Optimize model inference latency and throughput for high-volume production serving (TensorRT-LLM, vLLM, Triton Inference Server).
- Architect and maintain MLOps pipelines: data versioning, experiment tracking, automated evaluation, and CI/CD for model deployment (Kubeflow, Argo, MLflow).
- Collaborate with Research Scientists to translate novel architectures from prototype to robust, production-grade code.
- Lead efforts in model quantization, distillation, and compilation for efficient deployment on GPU/TPU clusters.
- Establish best practices for data quality, lineage, and governance at petabyte scale.
- Mentor junior engineers and drive technical strategy for the ML Platform team.
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 preferred, JAX/TensorFlow a plus).
- Proven track record of training and deploying large-scale models (>10B params) on distributed GPU clusters (Slurm, Kubernetes, Ray).
- Deep understanding of Transformer architecture internals, attention mechanisms, and optimization techniques (FlashAttention, RoPE, Grouped-Query Attention).
- Strong systems engineering skills: Linux kernel, CUDA programming, networking (InfiniBand/RDMA), and container orchestration.
- Experience with modern MLOps toolchains (Weights & Biases, Dagster, Terraform, Docker).
- Excellent communication skills with the ability to distill complex technical concepts for cross-functional stakeholders.