- Company Name
- Rivet Industries
- Job Title
- Machine Learning Researcher / ML-Ops Engineer
- Job Description
-
Job Title: Machine Learning Researcher / ML-Ops Engineer
Role Summary: Conduct research and develop production-ready machine learning pipelines for computer vision, sensor fusion, and pose estimation on embedded platforms, ensuring real‑time performance and robust MLOps workflows.
Expactations: Deliver prototype‑to‑production roadmaps, validate novel algorithms against state‑of‑the‑art benchmarks, publish findings, and influence product roadmaps through actionable insights.
Key Responsibilities:
- Build and evaluate POCs in Python/C++ on embedded hardware.
- Design, prototype, and optimize imaging and video pipelines for AR/VR applications.
- Create end‑to‑end ML pipelines using PyTorch/TensorFlow Lite, including automated data preprocessing and augmentation.
- Apply and research quantization, pruning, and other edge‑deployment techniques to achieve real‑time inference on mobile and embedded devices.
- Implement MLOps best practices: model versioning, monitoring, CI/CD, and containerization (Docker, Kubernetes).
- Document research outcomes, produce reproducible experiments, and map prototypes to production readiness.
- Stay current with academic literature and integrate cutting‑edge techniques into solutions.
- Collaborate with cross‑functional teams to shape product and research direction.
Required Skills:
- Proficient Python development with deep learning frameworks (PyTorch, TensorFlow).
- Experience in ML pipeline construction, model deployment, and production monitoring.
- Knowledge of quantization, pruning, and edge deployment strategies.
- Strong background in computer vision: image processing, feature detection, optical flow, SLAM, and pose estimation.
- Familiarity with sensor fusion (IMU, camera, stereo).
- C++ programming for embedded system validation.
- MLOps tooling: Docker, Kubernetes, CI/CD pipelines, and model serving.
- Cloud experience (AWS, GCP, Azure) for ML workloads is desirable.
Required Education & Certifications:
- BS with 5+ years of machine learning research or applied ML engineering experience (or MS with 2+ years).
- Ph.D. in Computer Vision, Machine Learning, or related field preferred.
- Publications in top-tier conferences (CVPR, ICCV, ECCV, NeurIPS, ICML) are a plus.
- Certificates in relevant technologies (e.g., CUDA, Docker, Kubernetes) are advantageous.