Deskripsi Pekerjaan
At NexGen AI 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 your research translates directly into production systems used by Fortune 500 companies.
You will operate at the intersection of cutting-edge research and robust engineering, collaborating with a world-class team of researchers, data engineers, and product strategists. We value deep technical expertise, a bias for action, and a passion for pushing the boundaries of what LLMs and multimodal models can achieve in a production environment.
Why Join Us?
- Frontier Tech: Work with H100 clusters, custom model architectures, and petabyte-scale datasets.
- Ownership: End-to-end responsibility from experiment design to global deployment.
- Compensation: Top-of-market salary, significant equity (ISO), 401k matching, and comprehensive healthcare.
- Culture: Low bureaucracy, high trust, quarterly hackathons, and an annual offsite.
Tanggung Jawab
- Design, implement, and optimize distributed training pipelines for Large Language Models (LLMs) and Multimodal models on GPU clusters (Kubernetes/Slurm).
- Lead the full MLOps lifecycle: data curation, experimentation tracking (Weights & Biases/MLflow), model evaluation, quantization, and CI/CD for model deployment.
- Architect low-latency, high-throughput inference systems using TensorRT-LLM, vLLM, or custom CUDA kernels.
- Collaborate with Research Scientists to reproduce and productionalize novel architectures from top-tier conferences (NeurIPS, ICML, ICLR).
- Establish rigorous evaluation frameworks (automated benchmarks, human-in-the-loop RLHF/RLAIF) to measure model quality, safety, and alignment.
- Mentor junior engineers and conduct design reviews to ensure system scalability, reliability, and cost-efficiency.
- Drive innovation in parameter-efficient fine-tuning (LoRA/QLoRA), Retrieval-Augmented Generation (RAG), and agentic workflows.
Kualifikasi
- Education: M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience.
- Experience: 5+ years of industry experience building and deploying deep learning models at scale; 2+ years specifically with Transformer architectures (BERT, GPT, LLaMA, Mistral).
- Programming: Expert-level Python (PyTorch, JAX, or TensorFlow); strong proficiency in C++/CUDA or Rust for performance-critical paths.
- Infrastructure: Deep hands-on experience with distributed training (FSDP, DeepSpeed, Megatron-LM), Kubernetes, Docker, and cloud platforms (AWS/GCP/Azure).
- MLOps: Proven track record of building reproducible CI/CD/CT pipelines for ML (Kubeflow, Airflow, Argo).
- Optimization: Demonstrated expertise in model compression (quantization, distillation, pruning) and inference optimization (batching, caching, speculative decoding).
- Communication: Ability to articulate complex technical concepts to cross-functional stakeholders and contribute to technical blog posts or open-source projects.