Deskripsi Pekerjaan
Apex Neural Systems is building the next generation of generative AI infrastructure. We are looking for a Senior Machine Learning Engineer to lead the architecture and deployment of large-scale foundation models. This is a high-impact role where you will own the full ML lifecycle—from data curation and distributed training to low-latency serving optimization.
You will join a world-class team of researchers and engineers from Google Brain, Meta AI, and OpenAI. We offer a hybrid work model (3 days onsite in our SoMa headquarters), competitive equity, and a compute budget that removes bottlenecks to innovation.
Why This Role Matters
- Directly shape the core intelligence powering our enterprise product suite.
- Access to 10,000+ H100 GPUs for unrestricted experimentation.
- Publish at top-tier conferences (NeurIPS, ICML, ICLR) with company support.
Tanggung Jawab
- Design, train, and optimize large language models (LLMs) and multimodal architectures at scale (10B+ parameters).
- Build and maintain robust MLOps pipelines for continuous training, evaluation, and deployment using Kubernetes and Kubeflow.
- Implement advanced distributed training techniques (FSDP, DeepSpeed, Megatron-LM) to maximize GPU utilization.
- Collaborate with Product and Research to define model capabilities, evaluation benchmarks, and safety guardrails.
- Optimize inference latency and throughput via quantization (GPTQ, AWQ), speculative decoding, and custom CUDA kernels.
- Mentor junior engineers and establish best practices for code quality, reproducibility, and experiment tracking.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, or related quantitative field (or equivalent industry experience).
- 5+ years of professional experience training and deploying deep learning models in production environments.
- Expert proficiency in Python, PyTorch/JAX, and the Hugging Face ecosystem (Transformers, Accelerate, PEFT).
- Proven track record with distributed training on large GPU clusters (SLURM, Kubernetes, Ray).
- Deep understanding of Transformer architecture, attention mechanisms, and scaling laws.
- Strong software engineering fundamentals: Git, CI/CD, Docker, profiling tools (NVIDIA Nsight, PySpy).