Deskripsi Pekerjaan
About the Role
We are seeking a visionary Senior Machine Learning Engineer to join our core research team at Nexus Intelligence Labs. You will architect and deploy state-of-the-art models that power our next-generation AI products, impacting millions of users globally. This is a high-autonomy role for an engineer who thrives at the intersection of research and production.
Why Join Us?
- Frontier Tech: Work with LLMs, Diffusion Models, and Reinforcement Learning.
- Ownership: End-to-end lifecycle from data strategy to production monitoring.
- Compensation: Top-tier salary, significant equity refreshers, and premium benefits.
- Flexibility: Hybrid schedule (3 days onsite in SoMa) or remote-eligible for exceptional candidates.
Tanggung Jawab
- Design, train, and optimize large-scale deep learning models (Transformers, CNNs, GNNs) for production environments.
- Build and maintain robust MLOps pipelines for automated training, evaluation, versioning, and deployment (CI/CD/CT).
- Collaborate with Product and Research to translate business requirements into scalable ML solutions.
- Conduct rigorous A/B testing and offline evaluation to measure model performance and business impact.
- Mentor junior engineers and establish best practices for code quality, reproducibility, and model governance.
- Research and prototype novel architectures to solve complex problems in NLP, Computer Vision, or Multimodal AI.
- Optimize inference latency and cost through quantization, distillation, and hardware-aware compilation (TensorRT, ONNX).
- Ensure data privacy, security, and ethical AI compliance across all model lifecycles.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience.
- 5+ years of industry experience deploying ML models at scale in a production environment.
- Expert proficiency in Python and deep learning frameworks (PyTorch preferred, TensorFlow/JAX a plus).
- Strong software engineering fundamentals: Git, Docker, Kubernetes, CI/CD, Testing, System Design.
- Proven track record with MLOps tools (MLflow, Kubeflow, Airflow, Weights & Biases) and Cloud platforms (AWS/GCP/Azure).
- Deep understanding of distributed training, GPU optimization, and memory management.
- Publications in top-tier venues (NeurIPS, ICML, ICLR, CVPR) or significant open-source contributions preferred.
- Excellent communication skills; ability to articulate complex technical concepts to non-technical stakeholders.