Deskripsi Pekerjaan
Nexus Intelligence Labs is seeking a visionary Senior Machine Learning Engineer to architect and deploy the next generation of scalable AI infrastructure. We are a Series C startup backed by top-tier VCs (Sequoia, a16z), building foundational models for enterprise automation. You will own the end-to-end lifecycle of models—from data curation and distributed training to low-latency serving and MLOps automation—directly impacting millions of users.
This is a high-autonomy role for an engineer who thrives at the intersection of research and production engineering. You will collaborate with Research Scientists to productionize novel architectures and with Platform Engineers to define the GPU compute substrate. We value clean code, rigorous experimentation, and a product-first mindset.
Why Join Us?
- Technical Ownership: Greenfield architecture on massive GPU clusters (H100/A100).
- Research-to-Production Pipeline: Publish at top conferences (NeurIPS, ICML, ICLR) while shipping product.
- Competitive Package: Top-of-market salary, significant equity (ISO), 401k match, and comprehensive health/wellness benefits.
- Hybrid Flexibility: 3 days/week in our SoMa flagship office, 2 days remote.
Tanggung Jawab
- Design, implement, and optimize distributed training pipelines for Large Language Models (LLMs) and Vision Transformers using PyTorch, FSDP/DeepSpeed, and JAX/Flax.
- Architect high-throughput, low-latency model serving infrastructure (TensorRT-LLM, vLLM, Triton Inference Server) supporting real-time and batch inference at scale.
- Build and maintain the MLOps flywheel: automated data versioning (DVC/LakeFS), experiment tracking (MLflow/Weights & Biases), CI/CD for models (Kubeflow/Argo), and automated model monitoring/drift detection.
- Collaborate with Research Scientists to translate novel architectures (Mixture of Experts, RAG, Agentic workflows) into robust, production-grade services.
- Optimize GPU utilization and cost efficiency across multi-cloud (AWS/GCP) and on-prem clusters using Kubernetes (EKS/GKE) and Slurm.
- Establish best practices for code quality, testing (pytest, property-based testing), and reproducible research environments (Docker, Nix, Conda).
- Mentor junior ML engineers and contribute to technical strategy, hiring, and architectural review boards.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience (5+ years industry ML engineering).
- Expert proficiency in Python and modern ML stack (PyTorch, Hugging Face Transformers, JAX/TensorFlow).
- Proven track record of deploying and operating Generative AI / LLMs in high-availability production environments.
- Deep expertise in distributed systems concepts: data/model parallelism, gradient accumulation, kernel fusion, and CUDA optimization.
- Strong software engineering fundamentals: Linux internals, networking, containerization (Docker/K8s), and infrastructure-as-code (Terraform/Pulumi).
- Experience with Data Lake architectures (Iceberg, Delta Lake, Spark/Ray) for petabyte-scale unstructured data processing.
- Demonstrated ability to debug complex performance bottlenecks using profilers (Nsight, Py-Spy, eBPF).
- Excellent communication skills; ability to distill complex technical concepts for cross-functional stakeholders.