Deskripsi Pekerjaan
Shape the Future of Generative AI
At Nexus Intelligence, we are building the cognitive infrastructure for the next generation of autonomous agents. We are looking for a Senior Machine Learning Engineer to architect, train, and deploy state-of-the-art Large Language Models (LLMs) at scale. This is a high-impact role where your work directly defines product capabilities used by millions.
You will operate at the intersection of research and production, turning novel architectures into robust, low-latency services. We value deep technical ownership, a bias for action, and a passion for pushing the boundaries of what LLMs can achieve in real-world environments.
Tanggung Jawab
- Design and implement end-to-end ML pipelines for training, fine-tuning, and evaluating LLMs (7B–70B+ parameters).
- Architect scalable MLOps infrastructure on Kubernetes (EKS/GKE) for model serving, monitoring, and continuous evaluation.
- Pioneer RAG (Retrieval-Augmented Generation) and agentic workflows to solve complex reasoning tasks.
- Optimize model inference latency and throughput using quantization (GPTQ/AWQ), speculation decoding, and custom kernels (Triton/CUDA).
- Collaborate with Product and Research to define data strategies, curate high-quality datasets, and run rigorous ablation studies.
- Mentor junior engineers and establish best practices for code quality, testing, and reproducible experiments.
- Stay at the bleeding edge of GenAI literature (ICLR, NeurIPS, ICML) and rapidly prototype promising techniques.
Kualifikasi
- 5+ years of industry experience in ML engineering; 2+ years specifically deploying LLMs/Generative AI in production.
- Expert proficiency in Python, PyTorch/JAX, and Hugging Face ecosystem (Transformers, Accelerate, PEFT, TRL).
- Deep understanding of Transformer architecture, attention mechanisms, and distributed training (FSDP, DeepSpeed, Megatron-LM).
- Proven track record optimizing GPU utilization (A100/H100 clusters) and reducing inference costs at scale.
- Strong software engineering fundamentals: CI/CD (GitHub Actions/GitLab), Docker, Kubernetes, Terraform.
- Experience with vector databases (Pinecone, Weaviate, Milvus) and advanced RAG patterns (HyDE, Re-rankers, Knowledge Graphs).
- Advanced degree (MS/PhD) in Computer Science, ML, or equivalent practical experience with top-tier publications or open-source contributions.