Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are architecting the future of autonomous decision-making systems. We are seeking a Senior Machine Learning Engineer to lead the end-to-end lifecycle of large-scale models that power our core products. This is a high-impact role where you will bridge the gap between cutting-edge research and robust production systems, working alongside a world-class team of researchers and engineers in our San Francisco headquarters.
You will own the strategy for model architecture, training infrastructure, and MLOps pipelines, ensuring we deliver state-of-the-art performance with reliability and scale. If you are passionate about pushing the boundaries of Generative AI and Large Language Models (LLMs) in a fast-paced, well-funded environment, we want to hear from you.
Tanggung Jawab
- Design, train, and optimize large-scale deep learning models (LLMs, Diffusion, Transformers) for production deployment.
- Build and maintain scalable MLOps pipelines for data ingestion, training, evaluation, and deployment using Kubernetes, Kubeflow, or Airflow.
- Collaborate with Research Scientists to reproduce and productionize novel algorithms from top-tier conferences (NeurIPS, ICML, ICLR).
- Implement distributed training strategies (FSDP, DeepSpeed, Megatron-LM) across multi-node GPU clusters (A100/H100).
- Establish rigorous model evaluation frameworks, including automated testing, A/B testing infrastructure, and drift monitoring.
- Optimize inference latency and cost through quantization, distillation, and custom kernel development (CUDA/Triton).
- Mentor junior engineers and define best practices for code quality, reproducibility, and experiment tracking.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience.
- 5+ years of industry experience building and deploying ML systems at scale.
- Expert proficiency in Python and deep learning frameworks (PyTorch preferred, TensorFlow/JAX a plus).
- Proven track record with LLM training, fine-tuning (LoRA, RLHF, DPO), and alignment techniques.
- Deep understanding of distributed systems, GPU architecture, and performance profiling tools (NVIDIA Nsight, PyTorch Profiler).
- Strong MLOps experience: Docker, Kubernetes, CI/CD (GitLab/GitHub Actions), Feature Stores, Vector Databases.
- Publications in top-tier venues or significant open-source contributions to ML infrastructure projects.
- Excellent communication skills; ability to translate complex technical concepts to cross-functional stakeholders.