Data Engineering Leader for Machine Learning Infrastructure
hace 5 días
About the Role
We are seeking a highly skilled MLOps Engineer to bridge the gap between data science and operations. This role involves building and maintaining automated infrastructure, pipelines, and processes for deploying and monitoring machine learning models in production environments.
Key Responsibilities:
- Infrastructure Development: Streamline preprocessing, model training, and validation for ML engineers using AWS infrastructure and transitioning to third-party tools or in-house solutions as necessary.
- Collaboration: Work closely with clinical partners and teammates across the United States.
- Design and Architecture: Create detailed architecture/design documents for owned components and participate in risk management and Threat Model Analysis (TMA).
- Backend Development: Develop dashboards, data pipelines, and infrastructure tailored to the ML team's unique requirements.
- Model Deployment: Design scalable systems for training and inference, including CI/CD pipelines and automation tools for deploying machine learning models.
- Quality Assurance: Develop automation frameworks, test plans, and unit/integration tests. Collaborate with QA teams for system testing.
- Team Support: Conduct timely design and code reviews and support peer testing.
- Regulatory Compliance: Research and adhere to software development processes mandated by regulatory authorities.
Estimated Salary: $180,000 - $220,000 per year
Skills and Expertise:
- 5+ years of experience creating ML infrastructure for deep learning models with large-scale data; 10+ years of software engineering experience.
- Deep expertise with AWS, including services such as SageMaker Studio, Kinesis, S3, EC2, Lambda, CloudWatch, EMR, and Elastic Docker Container.
- Strong understanding of ML concepts, including model selection, deep learning architectures, and hyperparameter tuning. Experience with modern ML frameworks such as PyTorch, TensorFlow, NumPy, and Scikit-learn. Knowledge of AI/robotics frameworks like OpenCV, ROS2, and Kaldi is preferred.
- Experience in data engineering with distributed processing and training.
- Familiarity with MLOps frameworks like Kubeflow, MLFlow, and Airflow.
- Proficiency with containerization and orchestration tools like Docker and Kubernetes.
- Knowledge of A/B testing and benchmarking model performance in production.
- Expertise in Python and related ML libraries; working knowledge of C++ is preferred.
- Experience in PyQt user interface development or willingness to learn.
- Background in IoT, edge computing, and/or robotic systems is a plus.
- Knowledge of HIPAA compliance and developing software/AI for medical devices is advantageous.
Why Join Us?
As a fast-moving, well-funded organization working on innovative technologies to improve lives, you will collaborate with a multidisciplinary team pushing the boundaries of technology and making a meaningful impact.
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