Deskripsi Pekerjaan
Shape the future of Generative AI at Nexus Intelligence Labs.
We are seeking a Senior Machine Learning Engineer to lead the architecture and deployment of our next-generation Large Language Model (LLM) platform. You will operate at the intersection of cutting-edge research and high-scale production engineering, solving complex problems in model optimization, distributed training, and real-time inference serving.
This is a high-impact role where your work directly powers products used by millions. We value deep technical expertise, a bias for action, and a passion for building robust, scalable AI systems. Join a team of published researchers and elite engineers backed by top-tier VCs in the heart of San Francisco's Financial District.
Why Nexus?
- Frontier Tech: Work with H100 clusters, custom kernels, and the latest model architectures (Mixture of Experts, RAG, Agentic workflows).
- Ownership: End-to-end ownership of the ML lifecycle: data curation → training → evaluation → deployment → monitoring.
- Culture: Low bureaucracy, high trust. We optimize for learning velocity and technical excellence.
- Compensation: Top-of-market salary, significant equity (ISO), 401k matching, comprehensive health, and a $3,000 annual learning stipend.
Tanggung Jawab
- Design, implement, and optimize distributed training pipelines for LLMs (10B+ parameters) using PyTorch, FSDP/DeepSpeed, and Triton kernels.
- Architect low-latency, high-throughput inference serving infrastructure (vLLM / TensorRT-LLM) supporting real-time streaming and batch workloads.
- Lead R&D initiatives for model compression (quantization, distillation, pruning) to reduce compute costs without sacrificing quality.
- Build and maintain robust ML Observability: automated evaluation harnesses, drift detection, and A/B testing frameworks.
- Collaborate with Product and Research teams to translate novel architectures (e.g., Mamba, RetNet, MoE) from paper to production-grade code.
- Establish best practices for MLOps: feature stores, data lineage, reproducibility, and CI/CD for model artifacts.
- Mentor junior engineers and conduct rigorous code/design reviews to elevate the team’s technical bar.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, or equivalent practical experience (5+ years industry ML engineering).
- Expert-level proficiency in Python and PyTorch/JAX; strong systems programming skills in C++/CUDA/Rust a major plus.
- Proven track record of training and deploying large-scale deep learning models on GPU clusters (multi-node, multi-GPU).
- Deep understanding of Transformer architecture internals, attention mechanisms, and memory optimization techniques (FlashAttention, PagedAttention).
- Hands-on experience with modern serving stacks (vLLM, TGI, Triton, TensorRT-LLM) and Kubernetes (KubeFlow, KServe).
- Strong grasp of data engineering at scale (Spark, Ray, Parquet/Arrow) for preprocessing massive datasets (TB/PB).
- Excellent communication skills; ability to distill complex technical concepts for cross-functional stakeholders.