Deskripsi Pekerjaan
At Nexus AI Labs, we are architecting the next generation of foundation models that power enterprise intelligence. We are looking for a Senior Machine Learning Engineer who thrives at the intersection of cutting-edge research and production-grade engineering. You will own the full lifecycle of large-scale model training, optimization, and deployment, working alongside a world-class team of researchers and engineers from top labs.
This is a high-impact role where your contributions directly shape product capabilities used by Fortune 500 companies. We offer a culture of radical ownership, compute abundance (dedicated H100 clusters), and a compensation package that reflects the premium nature of your expertise.
Why Join Us?
- Compute Freedom: Unrestricted access to massive GPU clusters for experimentation.
- Research-to-Production Velocity: Push code to production same day; minimal bureaucracy.
- Equity Upside: Significant pre-IPO equity grant with quarterly refreshers.
- Elite Peers: Collaborate with published authors from NeurIPS, ICML, and ICLR.
Tanggung Jawab
- Design, implement, and optimize distributed training pipelines for LLMs and multimodal models (10B+ parameters) using PyTorch, FSDP/DeepSpeed, and Megatron-LM.
- Lead model quantization, compilation (TensorRT-LLM, vLLM), and serving optimization for low-latency, high-throughput inference at scale.
- Architect robust MLOps infrastructure: automated evaluation harnesses, data flywheels, and continuous pre-training/fine-tuning workflows.
- Collaborate with Research Scientists to translate novel architectures (e.g., MoE, SSMs, RAG systems) from paper to production-ready code.
- Define and enforce software engineering best practices: code review, CI/CD, testing strategies, and observability for ML systems.
- Mentor junior engineers and drive technical strategy for the Model Engineering pillar.
- Publish findings internally and externally (blogs, open source contributions, top-tier conferences).
Kualifikasi
- 5+ years professional experience building and deploying Deep Learning systems in production environments.
- Expert proficiency in Python, PyTorch, and CUDA kernel optimization / Triton.
- Proven track record of training/fine-tuning Large Language Models (7B+ params) on multi-node GPU clusters (InfiniBand/RoCE).
- Deep understanding of Transformer architecture internals, attention mechanisms, and memory-efficient optimization (FlashAttention, PagedAttention).
- Strong systems engineering background: Linux kernel, networking (RDMA/NCCL), containerization (Docker/K8s), and GPU scheduling (Slurm/Kueue).
- Experience with modern LLM serving stacks (vLLM, TGI, Triton Inference Server) and optimization techniques (Speculative Decoding, Continuous Batching).
- Bachelor's/Master's/PhD in Computer Science, Machine Learning, or equivalent practical experience.