Deskripsi Pekerjaan
At Nexus Intelligence, we are architecting the future of autonomous decision-making systems. We are seeking a Senior Machine Learning Engineer to lead the design, training, and deployment of large-scale foundation models that power our core enterprise products.
This is a high-impact role where you will own the end-to-end ML lifecycle—from data curation and distributed training on massive GPU clusters to low-latency serving infrastructure. You will work alongside a world-class team of researchers and engineers from top-tier labs (Google Brain, FAIR, OpenAI alumni) to push the boundaries of what is possible with Generative AI and Reinforcement Learning.
We offer a culture of technical excellence, minimal bureaucracy, and the resources to tackle ambitious problems. If you want your code to run in production at scale and define the next era of AI applications, this is your opportunity.
Tanggung Jawab
- Architect and implement scalable ML pipelines for training and fine-tuning LLMs and multimodal models on distributed GPU clusters (100+ nodes).
- Optimize model inference latency and throughput using techniques like quantization, distillation, speculative decoding, and custom CUDA kernels.
- Design and maintain robust MLOps infrastructure (CI/CD for ML, feature stores, model registry, automated evaluation harnesses).
- Collaborate with Research Scientists to translate novel architectures from paper to production-grade code (PyTorch/JAX).
- Establish best practices for data quality, lineage, and governance across petabyte-scale datasets.
- Mentor junior engineers and drive technical strategy for the ML Platform team.
- Partner with Product to define ML-powered features and own the reliability/SLAs of model serving APIs.
Kualifikasi
- MSc or PhD in Computer Science, Machine Learning, or equivalent practical experience (5+ years industry ML engineering).
- Expert proficiency in Python and deep learning frameworks (PyTorch preferred, JAX/TensorFlow a plus).
- Proven track record of training and deploying Large Language Models (LLMs) or large-scale vision models in production environments.
- Deep expertise in distributed training frameworks (FSDP, DeepSpeed, Megatron-LM) and hardware acceleration (CUDA, Triton, TPUs).
- Strong software engineering fundamentals: clean architecture, testing, CI/CD, Docker, Kubernetes.
- Experience with cloud ML ecosystems (AWS SageMaker, GCP Vertex AI, or Azure ML) and infrastructure-as-code (Terraform).
- Publications at top-tier venues (NeurIPS, ICML, ICLR, CVPR) or significant open-source contributions to ML libraries.