Deskripsi Pekerjaan
At Nexus AI Labs, we are pioneering the next generation of intelligent systems that transform how enterprises leverage data. We are seeking a Senior Machine Learning Engineer to join our world-class research and engineering team in the heart of San Francisco. You will own the full lifecycle of ML models—from research and prototyping to production deployment and monitoring—solving complex, high-impact problems at scale.
This role offers significant autonomy, a competitive equity package, and the opportunity to publish at top-tier conferences (NeurIPS, ICML, CVPR). If you are passionate about pushing the boundaries of deep learning, MLOps, and large-scale distributed training, we want to hear from you.
Tanggung Jawab
- Design, develop, and deploy state-of-the-art machine learning models for production environments serving millions of requests daily.
- Lead the architecture and implementation of scalable ML pipelines, including feature engineering, model training, validation, and CI/CD automation (MLOps).
- Collaborate with product managers and data scientists to translate business requirements into technical specifications and measurable ML metrics.
- Optimize model inference latency and throughput using techniques such as quantization, distillation, and hardware acceleration (GPU/TPU).
- Establish best practices for experiment tracking, reproducibility, model versioning, and automated retraining workflows.
- Mentor junior engineers, conduct code reviews, and contribute to the team's technical roadmap and hiring bar.
- Stay current with emerging research; evaluate and integrate novel architectures (e.g., Transformers, Diffusion Models, LLMs) into our stack.
- Ensure data privacy, security, and regulatory compliance (GDPR, CCPA) throughout the ML lifecycle.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related quantitative field.
- 5+ years of professional experience building and deploying production-grade ML systems at scale.
- Expert proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, JAX).
- Deep understanding of deep learning architectures (CNNs, RNNs, Transformers, GNNs) and optimization techniques.
- Strong software engineering fundamentals: OOP, design patterns, testing, Docker, Kubernetes, and Git.
- Hands-on experience with cloud platforms (AWS, GCP, or Azure) and distributed computing (Spark, Ray, Horovod).
- Proven track record of taking research projects from prototype to reliable, monitored production services.
- Excellent communication skills with the ability to articulate complex technical concepts to cross-functional stakeholders.