Deskripsi Pekerjaan
At Nexus AI Labs, we are pushing the boundaries of applied artificial intelligence to solve enterprise-scale problems. We are looking for a Senior Machine Learning Engineer to lead the design, development, and deployment of production-grade ML systems that power our core product suite.
You will work at the intersection of research and engineering, collaborating with a world-class team of researchers, data engineers, and product managers. This is a high-impact role where your architectural decisions directly shape the capabilities of our AI platform.
Why join us?
- Work on low-latency, high-throughput inference systems serving millions of requests daily.
- Access to massive proprietary datasets and dedicated GPU clusters (A100/H100).
- Culture of technical excellence: RFCs, design docs, and rigorous code review.
- Competitive equity package with quarterly liquidity events.
- Comprehensive benefits: 100% paid health premiums, 401k matching, unlimited PTO, and a $3,000 annual learning stipend.
Tanggung Jawab
- Architect and implement end-to-end ML pipelines: data ingestion, feature engineering, training, evaluation, and automated deployment (CI/CD for ML).
- Optimize model inference latency and throughput using techniques like quantization, distillation, TensorRT/ONNX Runtime, and custom CUDA kernels.
- Design scalable feature stores and offline/online feature consistency mechanisms.
- Establish and enforce MLOps best practices: model monitoring (drift detection), automated retraining triggers, and A/B testing frameworks.
- Collaborate with Research Scientists to productionalize novel architectures (LLMs, Diffusion Models, Graph Neural Networks).
- Mentor junior engineers and define the technical roadmap for the ML Platform team.
- Contribute to open-source tooling and publish findings at top-tier conferences (NeurIPS, ICML, KDD).
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience.
- 5+ years of professional experience building and deploying ML systems at scale in a production environment.
- Expert proficiency in Python and modern ML frameworks (PyTorch, TensorFlow/Keras, JAX).
- Deep understanding of distributed training (FSDP, DeepSpeed, Megatron-LM) and GPU optimization.
- Strong software engineering fundamentals: Type-safe Python (mypy/pydantic), Docker, Kubernetes, GitOps (ArgoCD/Flux), and Infrastructure as Code (Terraform).
- Proven experience with Cloud ML platforms (Vertex AI, SageMaker, Azure ML) or building internal platforms on AWS/GCP/Azure.
- Track record of shipping complex projects cross-functionally and communicating technical trade-offs to non-technical stakeholders.