Deskripsi Pekerjaan
At Nexus AI Labs, we are pioneering the next generation of generative AI infrastructure. We are seeking a Senior Machine Learning Engineer to architect, train, and deploy large-scale foundation models that power our enterprise product suite. This is a high-impact role where you will own the full ML lifecycle—from data curation and distributed training to low-latency serving and rigorous evaluation.
You will collaborate with a world-class team of researchers and engineers to push the boundaries of model efficiency, alignment, and scalability. If you are passionate about turning cutting-edge research into robust, production-grade systems that serve millions of users, we want to hear from you.
What We Offer
- Competitive equity package with early-exercise options
- Comprehensive health, dental, and vision coverage (100% employer-paid premiums)
- $3,000 annual learning & development stipend
- Flexible hybrid schedule (3 days onsite in our SoMa headquarters)
- State-of-the-art GPU compute budget (dedicated H100/A100 clusters)
Tanggung Jawab
- Design and implement distributed training pipelines for LLMs and multimodal models on thousands of GPUs using PyTorch FSDP/DeepSpeed.
- Optimize model inference latency and throughput via quantization (GPTQ, AWQ), compilation (TensorRT-LLM, vLLM), and custom CUDA kernels.
- Build and maintain petabyte-scale data processing pipelines (Spark/Ray) for pre-training, fine-tuning, and RLHF datasets.
- Develop rigorous evaluation frameworks (automated benchmarks, human preference labeling) to measure model quality, safety, and hallucination rates.
- Collaborate with Product and Research to define model requirements, iterate on architecture, and ship features on a 2-week sprint cadence.
- Mentor junior engineers and establish best practices for MLOps, reproducibility, and experiment tracking (MLflow/Weights & Biases).
- Contribute to open-source tooling and publish findings at top-tier venues (NeurIPS, ICML, ICLR).
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, or equivalent practical experience (5+ years industry ML engineering).
- Expert-level proficiency in Python and PyTorch/JAX; strong systems programming skills in C++/CUDA/Rust.
- Proven track record training and deploying models with 10B+ parameters in production environments.
- Deep understanding of Transformer architectures, attention mechanisms, and scaling laws.
- Hands-on experience with distributed training frameworks (FSDP, DeepSpeed, Megatron-LM) and cluster orchestration (Slurm, Kubernetes/Kubeflow).
- Strong grasp of MLOps principles: CI/CD for ML, model registry, feature stores, and monitoring (Prometheus/Grafana).
- Excellent communication skills; ability to translate complex technical concepts to cross-functional stakeholders.