Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are architecting the next generation of foundation models that power enterprise decision-making. We are looking for a Senior AI/ML Engineer who thrives at the intersection of cutting-edge research and scalable production systems. You will own the full lifecycle of model development—from data curation and distributed training to low-latency serving infrastructure.
This is a high-impact role where your work directly shapes our core product. We value deep technical expertise, a bias for action, and the ability to translate complex theoretical concepts into robust, maintainable code. Join a team backed by top-tier VCs, solving problems that redefine what’s possible with LLMs and multimodal architectures.
Tanggung Jawab
- Design, train, and optimize large-scale transformer models (LLMs, Vision-Language models) on distributed GPU clusters (100+ nodes).
- Build and maintain high-throughput, low-latency model serving infrastructure using TensorRT-LLM, vLLM, or custom CUDA kernels.
- Lead the data flywheel: curate massive datasets, implement RLHF/RLAIF pipelines, and design rigorous evaluation frameworks.
- Collaborate with Product and Research to define model roadmaps, balancing SOTA performance with inference cost constraints.
- Establish MLOps best practices: CI/CD for ML, automated retraining triggers, model monitoring, and drift detection.
- Mentor junior engineers and conduct deep-dive code reviews focusing on performance, numerical stability, and readability.
- Publish findings internally and externally (blog posts, conferences like NeurIPS, ICML, ICLR) to build brand authority.
Kualifikasi
- 5+ years of industry experience in Machine Learning Engineering; advanced degree (MS/PhD) in CS, Statistics, or related field preferred.
- Expert-level proficiency in Python and PyTorch/JAX; strong systems programming skills in C++/CUDA/Rust.
- Proven track record of training/fine-tuning models >7B parameters on multi-node clusters (Megatron-DeepSpeed, FSDP, YaFSDP).
- Deep understanding of GPU architecture, kernel fusion, quantization (GPTQ, AWQ), and speculative decoding.
- Experience with Kubernetes (Kubeflow, KServe), Terraform, and cloud platforms (AWS/GCP/Azure) for ML workloads.
- Strong publication record or significant open-source contributions to ML frameworks (PyTorch, HuggingFace, JAX, Triton).
- Excellent communication skills; ability to distill complex technical trade-offs for cross-functional stakeholders.