Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are architecting the future of Generative AI. We are seeking a Senior Machine Learning Engineer to lead the development of our core foundation models and deployment infrastructure. This is a high-impact role where you will own the full lifecycle of model development—from research and experimentation to production scaling—directly shaping products used by millions.
We offer a hybrid work culture (3 days onsite in our stunning Financial District office), competitive equity, and a generous budget for continuous learning and conference attendance. If you thrive on solving ambiguous, complex problems at the cutting edge of LLMs and multimodal systems, we want to talk to you.
Tanggung Jawab
- Design, train, and optimize large-scale foundation models (LLMs, Multimodal) for high-availability production environments.
- Architect robust MLOps pipelines for data ingestion, distributed training, evaluation, and automated deployment (CI/CD for ML).
- Conduct novel research in alignment, reasoning, and inference efficiency (quantization, distillation, speculative decoding).
- Collaborate closely with Product and Platform Engineering to define model requirements and integrate AI capabilities into user-facing features.
- Mentor junior engineers and establish best practices for code quality, reproducibility, and rigorous experimentation.
- Stay at the forefront of AI literature; reproduce, evaluate, and adapt SOTA papers (NeurIPS, ICML, ICLR) for internal applications.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical industry experience.
- 5+ years of industry experience training, evaluating, and deploying deep learning models at scale.
- Expert proficiency in Python, PyTorch (or JAX/TensorFlow), and distributed training frameworks (FSDP, DeepSpeed, Megatron-LM).
- Proven track record of optimizing model inference latency and throughput on GPU clusters (CUDA/Triton kernel experience a significant plus).
- Deep theoretical and practical understanding of Transformer architecture, attention mechanisms, and scaling laws.
- Strong experience with cloud infrastructure (AWS preferred) and container orchestration (Kubernetes, Docker).
- Exceptional communication skills; ability to translate complex technical concepts to cross-functional stakeholders and leadership.