Senior Machine Learning Engineer, H2O.ai

• Master's degree in Computer Science, Engineering, Physics, Mathematics, or related technical field
• 7+ years of experience in machine learning engineering, software development, or related roles
• 5+ years of experience building and deploying production ML systems
• Proven track record of leading technical projects and mentoring team members
• Expert-level proficiency in Python with strong knowledge of Bash, SQL, C/C++
• Deep experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn
• Extensive experience with data processing libraries: NumPy, Pandas, Matplotlib
• Hands-on experience with Hugging Face ecosystem and modern NLP/LLM tools
• Strong experience with containerization and orchestration: Docker, Kubernetes
• Knowledge of cloud platforms: AWS, GCP, Azure and their ML services
• Experience with MLworkflow orchestration tools: Airflow, Kubeflow, MLflow
• Proficiency in Infrastructure as Code: Terraform, CloudFormation
• Experience with monitoring and observability tools: Prometheus, Grafana, ELK stack
• Proven expertise in generative AI including diffusion models, GANs, VAEs, and normalizing flows
• Experience with large language models (LLMs) and agentic AI systems
• Knowledge of advanced architectures: CNNs, U-Nets, transformers, and attention mechanisms
• Experience with model optimization techniques: quantization, pruning, distillation
• Understanding of distributed training and inference systems
• Strong software development practices including version control, testing, and code review
• Experience with micro services architecture and API development
• Knowledge of database systems and data storage solutions
• Understanding of distributed systems and concurrent programming
• Experience with performance profiling and optimization
• Experience designing large-scale ML systems and data pipelines
• Knowledge of real-time and batch processing architectures
• Understanding of model serving patterns and inference optimization
• Experience with auto-scaling and resource management in production environments
• Knowledge of security best practices for ML systems
• Track record of solving complex technical problems with innovative engineering solutions
• Experience working with real-world, noisy datasets across multiple domains
• Ability to achieve significant performance improvements and system optimizations
• Strong debugging and troubleshooting skills for production ML systems
• Experience with A/B testing and experimentation frameworks
• Experience with edge deployment and model optimization for mobile/IoT devices
• Knowledge of multi-cloud and hybrid cloud architectures
• Background in streaming data processing and real-time ML systems
• Experience with graph neural networks and knowledge graphs
• Understanding of reinforcement learning and multi-agent systems
• Experience mentoring engineering teams and establishing technical standards
• Strong project management skills with experience in Agile/Scrum methodologies
• Ability to communicate complex technical concepts to diverse audiences
• Experience with technical writing and documentation
• Track record of driving technical innovation and process improvements

Desirable

• We are seeking a Senior Machine Learning Engineer with exceptional technical expertise in deploying, scaling, and maintaining production ML systems
• This role requires a strong combination of software engineering skills, ML/AI knowledge, and system architecture experience to build robust, scalable machine learning infrastructure
• The ideal candidate will have experience with end-to-end ML pipelines, modern MLOps practices, and the ability to bridge research and production environments
• Design and implement end-to-end machine learning pipelines from research to production
• Build scalable ML infrastructure supporting multiple models and high-throughput inference
• Develop automated systems for model training, validation, deployment, and monitoring
• Create efficient data processing pipelines with multiprocessing optimization and performance tuning
• Architect feature stores, model registries, and ML metadata management systems
• Deploy and maintain production ML models with focus on reliability, scalability, and performance Implement MLOps best practices including CI/CD for ML, automated testing, and model versioning
• Monitor model performance, data drift, and system health in production environments
• Optimize model inference for latency and throughput requirements
• Manage model lifecycle including retraining, rollback, and A/B testing strategies
• Implement cutting-edge ML techniques including generative AI, diffusion models, and large
• Language models
• Develop and optimize deep learning models using modern frameworks (TensorFlow,
• PyTorch)Build systems for handling multimodal data (text, images, video, time-series)
• Create solutions for challenging ML problems including out-of-distribution detection and feature alignment
• Implement efficient algorithms achieving significant performance improvements (orders of
• Magnitude speedups)
• Lead technical design reviews and architecture decisions for ML systems
• Mentor junior engineers and data scientists on ML engineering best practices
• Collaborate with research teams to transition experimental models to production
• Work with infrastructure teams to ensure optimal resource utilization and scaling
• Provide technical guidance on complex ML system design and implementation