Deskripsi Pekerjaan
Nexus Intelligence Labs is pioneering the next generation of autonomous decision-making systems. We are seeking a Senior Machine Learning Engineer to architect and deploy large-scale 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 productionization and monitoring—working alongside a world-class team of researchers and engineers.
We value intellectual honesty, rigorous engineering, and a bias for action. If you thrive in an environment where the boundary between research and production is blurred, and you want your work to directly shape the future of Applied AI, we want to hear from you.
What We Offer
- Competitive Base Salary ($180k–$250k) + Significant Early-Stage Equity
- Comprehensive Health, Dental & Vision (100% Employer Paid Premiums)
- Annual Learning & Development Stipend ($5,000)
- Flexible Hybrid Policy (3 days on-site in SoMa / 2 days remote)
- State-of-the-art GPU Compute Budget (H100/A100 Clusters)
Tanggung Jawab
- Design, train, and optimize large-scale deep learning models (LLMs, Diffusion, Transformers) for high-throughput, low-latency inference.
- Build and maintain robust MLOps pipelines (CI/CD/CT) for automated training, evaluation, versioning, and deployment using Kubernetes, Kubeflow, and MLflow.
- Collaborate with Product and Research teams to translate ambiguous business problems into well-defined ML tasks with clear success metrics.
- Implement advanced techniques for model compression, quantization, and distillation to optimize cost and latency on GPU/TPU hardware.
- Establish best practices for data quality, feature stores, experiment tracking, and model observability (drift detection, performance monitoring).
- Mentor junior engineers and conduct rigorous code/architecture reviews to elevate technical standards across the team.
- Stay at the forefront of ML literature; reproduce and adapt novel architectures (e.g., Mixture of Experts, RAG, Agentic Workflows) for production viability.
Kualifikasi
- MSc or PhD in Computer Science, Machine Learning, Statistics, or equivalent practical experience (5+ years industry ML experience).
- Expert proficiency in Python and deep learning frameworks (PyTorch preferred, TensorFlow/JAX a plus).
- Proven track record of taking models from prototype to production at scale (handling 1M+ requests/day).
- Deep understanding of distributed training (FSDP, DeepSpeed, Megatron-LM) and hardware acceleration (CUDA kernels, Triton, TensorRT).
- Strong software engineering fundamentals: Git, Docker, Kubernetes, CI/CD (GitLab/GitHub Actions), Clean Architecture, Testing.
- Experience with Modern Data Stack: Spark/Flink, Iceberg/Delta Lake, Feature Stores (Feast/Tecton), Vector Databases (Pinecone/Weaviate/Milvus).
- Publications at top-tier venues (NeurIPS, ICML, ICLR, CVPR) or significant open-source contributions to ML infrastructure projects are a strong plus.