Deskripsi Pekerjaan
Shape the Future of Generative AI
At Nexus Intelligence Labs, we are building the foundational models powering the next generation of enterprise automation. We are looking for a Senior Machine Learning Engineer to own the full lifecycle of large-scale model training, optimization, and deployment. This is a high-impact role where your work directly ships to millions of end-users.
You will join a world-class team of researchers and engineers from Google Brain, Meta AI, and OpenAI. We value deep technical rigor, speed of iteration, and a product-first mindset. If you thrive in ambiguity and want to push the boundaries of LLM reasoning and efficiency, this is your home.
Why Nexus?
- Compute Budget: Unrestricted access to H100/A100 clusters (10k+ GPUs).
- Open Culture: Publish at top-tier conferences (NeurIPS, ICML, ICLR); open-source contributions encouraged.
- Compensation: Top-of-market salary, significant equity (ISO), 401k matching, and comprehensive health/wellness.
- Flexibility: Hybrid schedule (3 days onsite in SoMa), relocation package available.
Tanggung Jawab
- Design, implement, and scale distributed training pipelines for LLMs (10B+ parameters) using PyTorch, FSDP, and Megatron-LM.
- Optimize model architectures for inference latency and throughput using TensorRT-LLM, vLLM, and custom CUDA kernels.
- Lead data curation strategies: preprocessing, deduplication, and synthetic data generation for pre-training and SFT/RLHF stages.
- Collaborate with Research Scientists to translate novel architectures (MoE, SSMs, RAG) from paper to production-grade code.
- Build and maintain rigorous evaluation frameworks (automated benchmarks, human preference annotation) to track model quality regressions.
- Mentor junior engineers and establish best practices for MLOps, experiment tracking (Weights & Biases/MLflow), and CI/CD for ML artifacts.
- Drive cost-efficiency initiatives across the training and serving infrastructure, targeting >30% FLOPs utilization.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, or equivalent practical experience (5+ years industry ML experience).
- Proven track record of training and deploying Large Language Models at scale (publications or production deployments required).
- Expert-level proficiency in Python, PyTorch, and the Hugging Face ecosystem (Transformers, Accelerate, PEFT).
- Deep understanding of GPU architecture, mixed-precision training, gradient accumulation, and distributed communication primitives (NCCL, Gloo).
- Strong software engineering fundamentals: Git, Docker, Kubernetes, Linux internals, and profiling tools (Nsight, py-spy).
- Experience with RLHF/RLAIF pipelines, preference optimization (DPO, PPO), and alignment techniques.
- Excellent communication skills; ability to distill complex technical concepts for cross-functional stakeholders.