Deskripsi Pekerjaan
At Nexus AI Labs, we are pushing the boundaries of generative AI and large language models to solve enterprise-scale challenges. We are seeking a Senior Machine Learning Engineer to lead the architecture, training, and deployment of next-generation foundation models.
You will work at the intersection of research and production, collaborating with a world-class team of researchers, data scientists, and platform engineers. This is a high-impact role where your work directly shapes our core product and the future of AI applications.
Why Nexus?
- Cutting-edge compute infrastructure (H100 clusters, dedicated research budget).
- Open publication policy & support for top-tier conference submissions (NeurIPS, ICML, ICLR).
- Competitive equity package with quarterly liquidity events.
- Comprehensive health, wellness, and learning stipends.
Tanggung Jawab
- Design, train, and optimize large-scale foundation models (LLMs, multimodal architectures) using distributed training frameworks (PyTorch FSDP, DeepSpeed, Megatron-LM).
- Build and maintain robust MLOps pipelines for data curation, experimentation, model evaluation, and CI/CD deployment to Kubernetes clusters.
- Lead the end-to-end lifecycle of model development: from problem definition and data strategy to production monitoring and iterative improvement.
- Conduct rigorous ablation studies and benchmarking to push SOTA performance on reasoning, coding, and agentic tasks.
- Collaborate with Product and Research teams to translate business requirements into scalable technical specifications.
- Mentor junior engineers and establish best practices for code quality, reproducibility, and model governance.
- Stay abreast of the latest literature (e.g., mixture-of-experts, retrieval-augmented generation, efficient attention mechanisms) and prototype promising techniques.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience (5+ years industry experience).
- Proven track record of training and deploying large deep learning models (>1B parameters) in a production environment.
- Expert-level proficiency in Python and deep learning frameworks (PyTorch preferred, JAX/TensorFlow a plus).
- Deep understanding of distributed systems concepts: GPU/TPU optimization, gradient accumulation, mixed precision training, and communication collectives (NCCL, Gloo).
- Strong experience with MLOps tooling: MLflow, Weights & Biases, Kubeflow, Airflow, or custom internal platforms.
- Solid software engineering fundamentals: clean architecture, testing (pytest), Docker, Kubernetes, and Git/GitHub workflows.
- Publications at top-tier venues (NeurIPS, ICML, ICLR, ACL, CVPR) or significant open-source contributions to ML libraries.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.