Deskripsi Pekerjaan
Nexus AI Labs is at the forefront of generative AI research, building foundation models that redefine enterprise automation. We are looking for a Senior Machine Learning Engineer to lead the full lifecycle of large-scale model training, optimization, and deployment.
You will work alongside a world-class team of researchers and engineers to push the boundaries of LLM efficiency, contributing directly to our core product roadmap. This is a high-impact role offering significant autonomy, a competitive equity package, and the compute resources necessary to solve hard problems at scale.
Why Join Us?
- Work on foundational model architecture, not just application layers.
- Access to dedicated H100/A100 clusters (10,000+ GPUs).
- Publication support for top-tier venues (NeurIPS, ICML, ICLR).
- Comprehensive health, wellness, and learning stipends.
Tanggung Jawab
- Design, implement, and optimize distributed training pipelines for Large Language Models (LLMs) using PyTorch, FSDP, and Megatron-LM.
- Conduct novel research into model architectures (Mixture of Experts, State Space Models) and training objectives to improve reasoning and efficiency.
- Build and maintain robust MLOps infrastructure for experiment tracking, model versioning, and automated evaluation pipelines.
- Collaborate with Research Scientists to translate theoretical prototypes into production-grade, scalable systems.
- Lead kernel-level optimization (CUDA/Triton) for custom attention mechanisms and activation functions.
- Establish rigorous evaluation benchmarks (MMLU, HumanEval, custom internal suites) to measure model quality and regression.
- Mentor junior engineers and define technical standards for code quality, testing, and documentation.
- Participate in on-call rotations for critical model serving infrastructure (SLA < 99.99%).
Kualifikasi
- M.Sc. or Ph.D. in Computer Science, Machine Learning, or equivalent practical experience (5+ years industry ML).
- Proven track record of training/fine-tuning models > 7B parameters on multi-node GPU clusters.
- Expert-level proficiency in Python, PyTorch, and distributed computing frameworks (DeepSpeed, Ray, JAX).
- Deep understanding of transformer architecture internals, quantization (GPTQ, AWQ), and speculative decoding.
- Strong systems programming skills (C++, CUDA, Triton) for high-performance kernel development.
- Experience with data curation pipelines at petabyte scale (Spark, Ray Data, Databricks).
- Publications in top-tier ML conferences (NeurIPS, ICML, ICLR, ACL) or significant open-source contributions (e.g., Hugging Face, vLLM, PyTorch).
- Excellent communication skills; ability to articulate complex technical trade-offs to cross-functional stakeholders.