Deskripsi Pekerjaan
Shape the Future of Applied AI
At Nexus Intelligence, we are building the decision-making brain for the world's most complex supply chains. We are looking for a Senior Machine Learning Engineer to own the end-to-end lifecycle of production-grade models that process petabytes of multimodal data in real-time.
This is a high-autonomy role for a builder who thrives at the intersection of deep learning research and robust distributed systems. You will not just tune hyperparameters—you will architect the ML platform, define MLOps strategy, and deploy models that directly drive millions in revenue for Fortune 500 clients.
Why Nexus?
- Research Freedom: Dedicated 20% time for exploring SOTA architectures (LLMs, Diffusion, GNNs) with a path to publication.
- Data Scale: Access to proprietary, high-fidelity industrial datasets (IoT, Satellite, Transactional).
- Modern Stack: Python, PyTorch, JAX, Kubernetes, Ray, Dagster, Weights & Biases, Snowflake.
- Compensation: Top-decile base, significant ISO equity, 401k match, unlimited PTO, and annual learning stipend.
We are a Series B startup ($45M raised) backed by Sequoia and a16z. Hybrid schedule (3 days onsite in SoMa). Visa transfer sponsorship available.
Tanggung Jawab
- Architect & Deploy: Design scalable training and inference pipelines for low-latency (<50ms) model serving using Kubernetes, Ray Serve, and TensorRT.
- Model Development: Lead the development of foundation models for time-series forecasting and anomaly detection, leveraging Transformers and Graph Neural Networks.
- MLOps Ownership: Build and maintain the CI/CD/CT (Continuous Training) infrastructure using Dagster/Airflow, MLflow, and Feature Stores (Feast/Tecton).
- Data-Centric AI: Collaborate with Data Engineering to construct high-quality training sets; implement active learning and data valuation loops to maximize model ROI.
- Technical Leadership: Mentor junior engineers, conduct rigorous code/design reviews, and establish best practices for reproducibility, monitoring (drift/bias), and governance.
- Cross-Functional Collaboration: Partner with Product and Domain Experts to translate ambiguous business problems into well-defined ML objectives with clear evaluation metrics.
- Innovation: Prototype and benchmark novel architectures (e.g., Mixture of Experts, State Space Models) for deployment on edge devices and cloud GPUs.
Kualifikasi
- Education: MS/PhD in Computer Science, Statistics, Mathematics, or equivalent practical experience.
- Experience: 5+ years professional ML engineering experience; 2+ years deploying models to high-scale production environments.
- Deep Learning Expertise: Expert-level proficiency in PyTorch or JAX; strong grasp of optimization, regularization, and distributed training (FSDP, DeepSpeed).
- Systems & Engineering: Strong software engineering fundamentals (Python, Rust/Go/C++ a plus); deep experience with Docker, Kubernetes, and Cloud (AWS/GCP).
- MLOps Mastery: Proven track record building automated retraining pipelines, feature stores, and model monitoring/alerting systems from scratch.
- Communication: Ability to articulate complex technical trade-offs to non-technical stakeholders and write clear technical documentation (RFCs, design docs).
- Preferred: Publications at top-tier venues (NeurIPS, ICML, ICLR, KDD) or significant open-source contributions to ML infrastructure tools.