Deskripsi Pekerjaan
At Nexus AI Labs, we are building the next generation of foundation models that power enterprise intelligence. We are looking for a Senior Machine Learning Engineer to join our core research team in the heart of San Francisco. You will own the full lifecycle of large-scale model training, from data curation and distributed training infrastructure to optimization and production deployment.
This is a high-impact role where your work directly shapes product capabilities used by Fortune 500 companies. We value deep technical expertise, a passion for solving hard systems problems, and the ability to translate cutting-edge research into robust, scalable engineering.
Why join us?
- Work on frontier LLM/MLM architectures with massive compute budgets.
- Collaborate with published researchers and world-class engineers.
- Competitive equity package, comprehensive health benefits, and 401k matching.
- Hybrid flexibility (3 days onsite in our SoMa office).
Tanggung Jawab
- Design, implement, and optimize distributed training pipelines for large language models (LLMs) on GPU clusters (1000+ GPUs).
- Develop custom kernels and optimization techniques (FlashAttention, quantization, sparsity) to maximize throughput and reduce memory footprint.
- Build and maintain scalable data processing pipelines (Petabyte-scale) for pre-training, fine-tuning, and RLHF.
- Collaborate with research scientists to prototype novel architectures and transition them into production-grade code.
- Establish rigorous evaluation frameworks and automated benchmarks for model quality and regression testing.
- Mentor junior engineers and define best practices for MLOps, reproducibility, and experiment tracking.
- Contribute to open-source tooling 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 experience).
- Expert proficiency in Python and Deep Learning frameworks (PyTorch preferred, JAX/TensorFlow a plus).
- Proven track record of training and deploying Large Language Models (7B+ parameters) in production environments.
- Deep understanding of distributed systems concepts: MPI/NCCL, FSDP/DeepSpeed, tensor/pipeline parallelism.
- Strong systems programming skills (CUDA, Triton, C++) for kernel-level optimization.
- Experience with MLOps stack: Kubernetes, Ray, MLflow, Weights & Biases.
- Excellent communication skills; ability to articulate complex technical trade-offs to cross-functional stakeholders.