Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are architecting the future of generative AI for enterprise automation. We are seeking a Senior Machine Learning Engineer to lead the design, training, and deployment of large-scale foundation models that power our core product suite. This is a high-impact role where you will own the full ML lifecycle—from data curation and distributed training on GPU clusters to low-latency inference optimization in production.
You will collaborate directly with Research Scientists, Platform Engineers, and Product Leads to translate cutting-edge research (LLMs, Diffusion, RAG architectures) into robust, scalable systems serving millions of API requests daily. We value engineering rigor, a bias for action, and a passion for pushing the boundaries of model efficiency.
Why Join Us?
- Work on frontier model architectures with direct product application.
- Access to dedicated H100/A100 clusters and a modern MLOps stack (Kubeflow, Ray, MLflow).
- Competitive equity package in a well-funded, pre-IPO startup.
- Hybrid flexibility: 3 days onsite in our SoMa headquarters, 2 days remote.
Tanggung Jawab
- Design and implement end-to-end ML pipelines for training, fine-tuning, and evaluating Large Language Models (LLMs) and multimodal models.
- Optimize model inference latency and throughput using techniques such as quantization (GPTQ/AWQ), distillation, speculative decoding, and TensorRT-LLM/vLLM integration.
- Architect scalable data processing frameworks (Spark/Ray) for petabyte-scale unstructured data curation, cleaning, and annotation workflows.
- Conduct rigorous offline/online evaluation (A/B testing, human preference alignment) to drive model quality improvements.
- Collaborate with Platform Engineers to build self-serve tooling for experiment tracking, model registry, and automated CI/CD for ML artifacts.
- Stay abreast of SOTA research (e.g., Mixture of Experts, State Space Models, Agentic Workflows) and prototype viable candidates for productionization.
- Mentor junior engineers and establish best practices for code quality, reproducibility, and responsible AI governance.
Kualifikasi
- 5+ years professional experience in Machine Learning Engineering; advanced degree (MS/PhD) in CS, Statistics, or related field preferred.
- Expert proficiency in Python and deep learning frameworks (PyTorch required, JAX/TensorFlow a plus).
- Proven track record of training and deploying LLMs (7B+ params) in production environments; experience with distributed training (FSDP, DeepSpeed, Megatron-LM).
- Deep understanding of GPU architecture, CUDA kernels, and memory management for high-performance inference.
- Strong software engineering fundamentals: Docker, Kubernetes, Git, CI/CD, and designing maintainable, testable systems.
- Experience with RAG systems, Vector Databases (Pinecone, Weaviate, Milvus), and Agent frameworks (LangChain, LlamaIndex, AutoGen).
- Excellent communication skills; ability to distill complex technical concepts for cross-functional stakeholders.