Deskripsi Pekerjaan
Are you ready to architect the next generation of intelligent systems? Nexus Intelligence Labs is seeking a Senior Machine Learning Engineer to join our core research team in the heart of San Francisco. We are building foundational models that redefine how enterprises interact with unstructured data.
In this role, you will own the end-to-end lifecycle of large-scale ML models—from data strategy and model architecture to deployment and monitoring. You will collaborate with world-class researchers and engineers to push the boundaries of NLP and Computer Vision, shipping products used by Fortune 500 companies globally.
We offer a culture of radical ownership, a compute budget that removes bottlenecks, and a compensation package that reflects the impact you will drive.
Tanggung Jawab
- Design, train, and optimize large-scale deep learning models (Transformers, Diffusion Models, LLMs) for production environments.
- Architect robust, scalable ML pipelines (data ingestion, feature engineering, training, evaluation, CI/CD) using Kubernetes and Kubeflow.
- Lead the transition of research prototypes into high-performance, low-latency production services (TensorRT, ONNX, Triton Inference Server).
- Establish best practices for MLOps: experiment tracking, model registry, automated retraining, and drift detection.
- Collaborate with Product and Research teams to define technical roadmaps and solve ambiguous, high-impact problems.
- Mentor junior engineers and conduct rigorous code/design reviews to maintain engineering excellence.
- Publish findings at top-tier conferences (NeurIPS, ICML, CVPR) and contribute to open-source initiatives.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience (5+ years industry experience).
- Expert proficiency in Python and deep learning frameworks (PyTorch preferred, TensorFlow/JAX a plus).
- Proven track record of deploying LLMs or large-scale vision models to production (experience with quantization, distillation, LoRA/PEFT).
- Deep understanding of distributed training (FSDP, DeepSpeed, Megatron-LM) and GPU optimization (CUDA kernels, Nsight).
- Strong software engineering fundamentals: Clean architecture, testing (pytest), version control, and CI/CD (GitLab/GitHub Actions).
- Experience with modern data stacks (Spark, Ray, Dagster/Airflow) and cloud platforms (AWS/GCP/Azure).
- Excellent communication skills; ability to articulate complex technical concepts to non-technical stakeholders.