Deskripsi Pekerjaan
Shape the Future of Generative AI
At Nexus Intelligence, we are building the cognitive layer for enterprise automation. We are looking for a Senior Machine Learning Engineer to architect, train, and deploy state-of-the-art Large Language Models (LLMs) and multimodal systems at scale. You will own the full ML lifecycle—from data curation and distributed training to low-latency serving infrastructure.
This is a high-impact role reporting directly to the CTO. You will have the autonomy to publish research, open-source tooling, and define the technical roadmap for our core AI platform. We operate in a hybrid model (3 days onsite in our SoMa office) to foster deep collaboration on complex infrastructure challenges.
Why Join Us?
- Frontier Tech: Work on 70B+ parameter models, custom CUDA kernels, and agentic RAG systems.
- Ownership: End-to-end responsibility for model performance, not just feature tickets.
- Compensation: Top-of-market base, significant equity (ISO), 401k match, and unlimited PTO.
- Compute Budget: Dedicated access to H100/A100 clusters; no ticket queues for GPU time.
Tanggung Jawab
- Design and implement distributed training pipelines for LLMs (7B–70B+ parameters) on GPU clusters (100+ nodes) using FSDP/DeepSpeed.
- Optimize model inference latency and throughput using quantization (GPTQ/AWQ), speculative decoding, and custom CUDA kernels.
- Architect robust MLOps pipelines: automated evaluation, CI/CD for models, feature stores, and model registry governance.
- Develop Retrieval-Augmented Generation (RAG) systems with advanced reranking, agentic workflows, and knowledge graph integration.
- Collaborate with Product to translate ambiguous business problems into measurable ML objectives and offline/online evaluation metrics.
- Mentor junior engineers; establish best practices for code quality, reproducibility, and experiment tracking (Weights & Biases / MLflow).
- Stay at the frontier of GenAI research; reproduce and productionize techniques from top-tier conferences (NeurIPS, ICML, ICLR).
Kualifikasi
- 5+ years industry experience in ML Engineering; 2+ years specifically on Large Language Models / Foundation Models.
- Expert proficiency in Python, PyTorch / JAX, and distributed training frameworks (FSDP, DeepSpeed, Megatron-LM).
- Deep understanding of Transformer architecture, attention mechanisms, RoPE, FlashAttention, and mixture-of-experts (MoE).
- Proven track record deploying high-throughput, low-latency ML services on Kubernetes (EKS/GKE) using Triton, vLLM, or TGI.
- Strong software engineering fundamentals: Git, CI/CD, Docker, system design, profiling, and debugging distributed systems.
- Experience with cloud GPU infrastructure (AWS p4d/p5, GCP A3) and cost-optimization strategies (spot instances, auto-scaling).
- Publications at top venues or significant open-source contributions (e.g., Hugging Face, vLLM, LangChain) highly preferred.
- MSc or PhD in Computer Science, Machine Learning, or equivalent quantitative field.