Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are architecting the next generation of adaptive AI systems that power real-time decision-making for Fortune 500 enterprises. We are looking for a Senior Machine Learning Engineer who thrives at the intersection of cutting-edge research and robust, scalable production engineering.
You will own the full lifecycle of ML models—from ideation and experimentation through deployment, monitoring, and continuous optimization. This is a high-impact role where your work directly shapes product strategy and delivers measurable ROI for our clients. We value deep technical expertise, a product mindset, and the ability to mentor junior engineers.
Why Join Us?
- Technical Excellence: Work with massive, proprietary datasets and state-of-the-art GPU clusters (H100s/A100s).
- Ownership: End-to-end ownership of critical ML pipelines; no handoff walls.
- Growth: Dedicated research budget ($5k/yr), conference sponsorships, and a clear path to Staff/Principal track.
- Culture: Low-meeting, high-autonomy environment. Hybrid schedule (3 days onsite in SoMa).
Tanggung Jawab
- Design, build, and maintain scalable ML pipelines (training, evaluation, feature engineering, serving) using Python, PyTorch/TensorFlow, and Kubernetes.
- Lead the architecture and deployment of LLMs and classic ML models for low-latency, high-throughput inference services.
- Collaborate with Product and Data Engineering to define model requirements, success metrics, and data contracts.
- Implement robust MLOps practices: CI/CD for ML (GitLab/Jenkins), automated retraining, drift detection, and A/B testing frameworks.
- Optimize model performance for cost and latency (quantization, distillation, ONNX Runtime, TensorRT).
- Conduct rigorous offline/online evaluation, including statistical significance testing and guardrail implementation for safety/bias.
- Mentor mid-level engineers; conduct code reviews and design reviews to elevate team technical standards.
- Stay current with SOTA research (Transformers, RAG, Agents, Multimodal) and prototype novel applications for product gaps.
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 production at scale.
- Programming: Expert-level Python (type-hinting, async, profiling); strong proficiency in SQL and at least one systems language (Go, Rust, C++).
- Frameworks: Deep expertise with PyTorch (preferred) or TensorFlow; Hugging Face ecosystem; LangChain/LlamaIndex for LLM apps.
- Infrastructure: Hands-on production experience with Kubernetes (EKS/GKE), Docker, Terraform, and Cloud (AWS/GCP); Spark/Ray for distributed training.
- MLOps: Proven track record building CI/CD/CT pipelines (MLflow, Kubeflow, Argo, or custom).
- Soft Skills: Exceptional communication skills; ability to translate complex technical concepts to non-technical stakeholders.