Deskripsi Pekerjaan
Join Nexus Intelligence Labs, a pioneering force in applied artificial intelligence, as we build the next generation of intelligent systems. We are seeking a Senior AI/ML Engineer to lead the design, development, and deployment of large-scale machine learning models that power our core products. You will operate at the intersection of research and engineering, transforming cutting-edge algorithms into robust, scalable production services.
Our culture thrives on intellectual curiosity, technical excellence, and a bias for action. You will collaborate with a world-class team of researchers, data scientists, and software engineers to solve complex, high-impact problems in natural language processing, computer vision, and recommender systems. If you are passionate about pushing the boundaries of what's possible with AI and delivering tangible value to millions of users, this is your arena.
Tanggung Jawab
- Architect and implement end-to-end ML pipelines: data ingestion, feature engineering, model training, evaluation, and CI/CD deployment.
- Lead the optimization of large language models (LLMs) and diffusion models for latency, throughput, and cost-efficiency on GPU clusters.
- Design and maintain feature stores, model registries, and automated retraining pipelines using MLOps best practices.
- Collaborate with product and research teams to translate ambiguous business requirements into well-defined ML problem statements.
- Conduct rigorous offline/online experimentation (A/B testing) to measure model impact on key business metrics.
- Mentor junior engineers, establish coding standards, and drive technical strategy for the ML platform.
- Stay abreast of SOTA advancements (Transformers, RAG, Agents, Quantization) and prototype innovative applications.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience.
- 5+ years of professional experience building and deploying production ML systems at scale.
- Expert proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, JAX, Hugging Face).
- Deep understanding of distributed training, quantization, distillation, and serving infrastructure (Triton, vLLM, TensorRT).
- Strong software engineering fundamentals: Git, Docker, Kubernetes, CI/CD, testing, and system design.
- Proven track record of taking research prototypes to high-availability production services.
- Excellent communication skills with the ability to influence cross-functional stakeholders.