Deskripsi Pekerjaan
Nexus AI Labs is at the forefront of generative AI research and production deployment. We are seeking a Senior Machine Learning Engineer to architect, train, and deploy state-of-the-art foundation models that power our enterprise product suite. You will work alongside a world-class team of researchers and engineers to push the boundaries of what is possible with large-scale deep learning.
This is a high-impact role offering significant autonomy, a competitive equity package, and the computational resources necessary to train models at the frontier of AI capability.
Tanggung Jawab
- Design and implement scalable training pipelines for large language models (LLMs) and multimodal architectures.
- Optimize model performance, latency, and cost-efficiency for high-throughput production inference.
- Lead the end-to-end MLOps lifecycle: data curation, experimentation, evaluation, deployment, and monitoring.
- Collaborate with Research Scientists to translate novel algorithms into robust, production-ready code.
- Build and maintain internal tooling for distributed training (e.g., PyTorch Distributed, DeepSpeed, Megatron-LM).
- Conduct rigorous ablation studies and benchmarking to guide architectural decisions.
- Mentor junior engineers and establish best practices for code quality, testing, and documentation.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, or a related quantitative field (or equivalent industry experience).
- 5+ years of professional experience developing and deploying deep learning models in a production environment.
- Expert-level proficiency in Python and deep learning frameworks (PyTorch strongly preferred, TensorFlow/JAX a plus).
- Proven experience with distributed training on GPU clusters (multi-node, multi-GPU) and mixed-precision training.
- Deep understanding of Transformer architectures, attention mechanisms, and optimization techniques (LoRA, RLHF, Quantization).
- Strong software engineering fundamentals: CI/CD, Docker/Kubernetes, Git, and cloud platforms (AWS/GCP/Azure).
- Publications at top-tier venues (NeurIPS, ICML, ICLR, CVPR) or significant open-source contributions are a major plus.