Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are architecting the next generation of autonomous decision-making systems. We are seeking a Senior Machine Learning Engineer to lead the design, training, and deployment of large-scale foundation models that power our core product suite. This is a high-impact role where you will own the full ML lifecycle—from data strategy and model architecture to production optimization and MLOps infrastructure.
You will collaborate directly with Research Scientists and Product Engineers to translate cutting-edge research (LLMs, Diffusion Models, RLHF) into robust, scalable services serving millions of requests daily. We value deep technical expertise, a bias for action, and the ability to mentor junior engineers in a fast-paced, innovative environment.
Why Join Us?
- Frontier Tech: Work on custom model architectures, not just API wrappers.
- Compute Budget: Access to dedicated H100/A100 clusters for training & inference.
- Ownership: End-to-end ownership of critical model pipelines.
- Compensation: Top-of-market salary, significant equity (ISO), 401k matching, and comprehensive health/wellness stipends.
Tanggung Jawab
- Design, implement, and optimize novel model architectures (Transformers, Mixture of Experts) for high-throughput, low-latency inference.
- Lead the end-to-end ML lifecycle: data curation, distributed training (FSDP/DeepSpeed), evaluation, quantization, and deployment via Kubernetes/Triton.
- Build and maintain scalable MLOps pipelines (feature stores, experiment tracking, CI/CD for models, automated retraining triggers).
- Conduct rigorous offline/online evaluation (A/B testing, shadow mode) to measure model quality, latency, and cost trade-offs.
- Collaborate with Product & Research to define model requirements, define SLAs, and rapidly prototype new capabilities (RAG, Agents, Tool Use).
- Mentor junior engineers on ML best practices, software engineering standards (Python, Rust, C++), and system design.
- Research and implement optimization techniques: Speculative Decoding, KV Cache Management, TensorRT-LLM / vLLM optimization.
- Ensure data privacy, security, and compliance (SOC2, GDPR) throughout the data and model pipeline.
Kualifikasi
- Education: M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience.
- Experience: 5+ years industry experience building and deploying production ML systems at scale; 2+ years specifically with Large Language Models or Generative AI.
- Programming: Expert-level Python (PyTorch/JAX/TensorFlow); proficiency in C++/Rust/CUDA for kernel-level optimization is a strong plus.
- Infrastructure: Deep hands-on experience with distributed training (Megatron-LM, DeepSpeed, FSDP) and serving stacks (vLLM, TGI, Triton, TensorRT-LLM) on Kubernetes (EKS/GKE).
- Data & Evaluation: Proven track record designing evaluation frameworks (human + automated) for generative tasks; experience with Petabyte-scale data processing (Spark, Ray, Flink).
- MLOps: Expertise in MLflow/Weights & Biases, Feature Stores (Feast/Tecton), and GitOps workflows (ArgoCD/Flux).
- Soft Skills: Exceptional communication skills; ability to distill complex technical concepts for cross-functional stakeholders.
- Publications/Open Source: Top-tier conference publications (NeurIPS, ICML, ICLR) or significant contributions to major open-source ML libraries preferred.