Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are architecting the next generation of autonomous decision-making systems. We aren't just deploying models; we are building the infrastructure that allows AI to reason, adapt, and scale in high-stakes environmentsâfrom algorithmic trading to robotic process automation.
We are looking for a Senior Machine Learning Engineer who thrives at the intersection of deep research and production-grade engineering. You will own the full lifecycle of complex ML systems: from ideation and dataset architecture to distributed training, low-latency serving, and rigorous MLOps observability. If you have a track record of shipping models that move business metrics and you obsess over model drift, feature stores, and GPU utilization, we need to talk.
Why this role is different: You will work with a world-class research team (published at NeurIPS, ICML, ICLR) but operate with the velocity of a Series B startup. You get unlimited compute budget, a modern stack (PyTorch, JAX, Ray, Kubernetes, Dagster), and zero legacy technical debt.
Tanggung Jawab
- Design, build, and optimize end-to-end ML pipelines for large-scale foundation models and task-specific fine-tunes.
- Architect robust feature engineering platforms and feature stores supporting real-time and batch inference.
- Lead distributed training strategies (FSDP, DeepSpeed, Megatron-LM) across multi-node GPU clusters (100+ H100s/A100s).
- Implement rigorous model evaluation frameworks: offline metrics, A/B testing infrastructure, shadow deployment, and champion/challenger analysis.
- Collaborate with Research Scientists to productionalize novel architectures (Mixture of Experts, RAG, Agentic workflows) with sub-100ms latency SLAs.
- Establish MLOps best practices: automated retraining triggers, data/ concept drift detection, model registry governance, and cost-optimized inference (TensorRT, vLLM, quantization).
- Mentor junior engineers and define the technical roadmap for the ML Platform team.
Kualifikasi
- 5+ years of industry experience deploying Deep Learning models in production environments (not just notebooks).
- Expert proficiency in Python, PyTorch/JAX, and the modern data stack (Dagster/Airflow, dbt, Snowflake/Databricks).
- Deep understanding of CUDA kernels, GPU memory management, and distributed systems fundamentals (NCCL, MPI, Ray).
- Proven experience with LLM fine-tuning (LoRA, QLoRA, DPO, RLHF) and efficient inference optimization (speculative decoding, KV cache management).
- Strong software engineering fundamentals: CI/CD (GitHub Actions/Buildkite), Containerization (Docker/K8s), Testing (pytest), and System Design.
- Track record of cross-functional leadershipâworking with Product, Data Engineering, and Infra to define ML strategy.
- Advanced degree (MS/PhD) in Computer Science, Statistics, or related field, or equivalent practical experience.