Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are building the next generation of autonomous AI agents that reason, plan, and execute complex workflows. 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 lifecycle—from data curation and distributed training to low-latency inference optimization—directly shaping products used by millions.
You will join a world-class team of researchers and engineers from top labs (Google Brain, FAIR, OpenAI) in our sunny SoMa headquarters. We offer a culture of intellectual honesty, minimal bureaucracy, and the compute budget to experiment fearlessly.
Tanggung Jawab
- Design, implement, and optimize distributed training pipelines for LLMs and multimodal models on GPU clusters (1000+ H100s).
- Lead model quantization, distillation, and compilation (TensorRT, Triton, vLLM) for sub-100ms latency serving.
- Architect robust evaluation frameworks (automated + human-in-the-loop) for alignment, safety, and hallucination detection.
- Collaborate with Product and Research to translate cutting-edge papers (e.g., mixture-of-experts, RLAIF, long-context) into production-grade features.
- Build and maintain the MLOps flywheel: data lineage, feature stores, experiment tracking, and automated retraining triggers.
- Mentor junior engineers and establish best practices for code quality, reproducibility, and GPU utilization.
- Contribute to open-source tooling and publish findings at top-tier conferences (NeurIPS, ICML, ICLR).
Kualifikasi
- 5+ years of industry experience shipping ML models to production; 2+ years specifically with Large Language Models.
- Deep expertise in PyTorch/JAX and distributed training frameworks (FSDP, DeepSpeed, Megatron-LM).
- Proven track record optimizing inference engines (CUDA kernels, TensorRT, custom operators) for throughput/latency.
- Strong systems engineering skills: Linux internals, networking (RDMA/InfiniBand), container orchestration (Kubernetes/Slurm).
- Experience with LLM alignment techniques: RLHF, DPO, Constitutional AI, or RLAIF.
- Bachelor's/Master's/PhD in Computer Science, Machine Learning, or equivalent quantitative field.
- Excellent communication skills; ability to distill complex technical trade-offs for cross-functional stakeholders.