Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are architecting the next generation of generative AI infrastructure. We are looking for a Senior Machine Learning Engineer to lead the design, training, and deployment of large-scale foundation models that power our enterprise product suite.
You will operate at the intersection of research and production, collaborating with a world-class team of researchers and engineers to push the boundaries of LLM efficiency, retrieval-augmented generation (RAG), and multi-modal systems. This is a high-impact role where your work directly shapes the core IP of a Series B startup backed by top-tier VCs (Sequoia, a16z).
Why join us?
- Technical Ownership: End-to-end ownership of model lifecycle—from data curation and distributed training to low-latency serving.
- Compute Budget: Access to massive H100/A100 clusters; we prioritize compute over headcount.
- Open Culture: Bi-weekly 'Paper & Code' sessions; strong commitment to open-source contributions.
- Compensation: Top-of-market base, significant equity refreshers, 401k matching, and comprehensive health/wellness stipends.
Tanggung Jawab
- Design, implement, and optimize distributed training pipelines for LLMs (10B+ parameters) using PyTorch, FSDP/DeepSpeed, and Megatron-LM.
- Architect high-throughput, low-latency inference systems (TensorRT-LLM, vLLM, Triton) supporting real-time streaming and batch workloads.
- Lead RAG and agentic workflow development: vector database optimization (Milvus, Pinecone), advanced chunking strategies, and reranking pipelines.
- Curate and process massive datasets (PB-scale) using Spark/Ray; implement data quality filtering, deduplication, and synthetic data generation loops.
- Collaborate with Research Scientists to reproduce, validate, and productionize novel architectures (e.g., Mixture of Experts, State Space Models).
- Establish MLOps best practices: automated evaluation harnesses (HELM, custom benchmarks), CI/CD for model artifacts, and comprehensive observability (Prometheus, Grafana, Weights & Biases).
- Mentor junior engineers and define the technical roadmap for the Model Platform team.
Kualifikasi
- Education: M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience.
- Experience: 5+ years of industry experience building and deploying ML systems at scale; 2+ years specifically with Large Language Models / Transformers.
- Distributed Training: Deep expertise in multi-node GPU training (NCCL, Gloo), memory optimization (activation checkpointing, CPU offloading), and kernel-level debugging.
- Inference Engineering: Proven track record optimizing transformer inference (quantization: GPTQ/AWQ/GGUF, KV cache management, continuous batching, speculative decoding).
- Programming: Expert-level Python (type-hinted, async), strong C++/CUDA fundamentals for custom kernel writing.
- Infrastructure: Kubernetes (operators, Kueue), Slurm, Terraform; Cloud (AWS/GCP/Azure) networking and storage (FSx for Lustre, S3/GCS).
- Software Engineering: Rigorous testing (pytest), code review culture, design patterns, and API design (gRPC, FastAPI).