Senior Analyst, Software Engineering
About The Position
The Software Engineering team designs, develops or coordinates development/troubleshooting or debugging of moderately complex software programs for enhancements and new products. Performs high-level design/modeling to convert stakeholder needs into software solutions. Develops software and tools in support of design, infrastructure and technology platforms. Determines hardware compatibility and/or influences hardware design. Develops the architectural guidelines, specifications, and technical standards and communicates to stakeholders. This role in summary The engineer will combine software engineering fundamentals with advanced machine learning techniques to build complex technical solutions for home appliances. The position requires execution across the machine learning lifecycle, from problem statement development, through data sourcing and cleaning, model training and compression, deployment, and field monitoring.
Requirements
- Bachelor's degree in computer science, engineering, statistics, physics, or a related technical field
- 5+ years software development experience (Python, Java, C++, etc), including contributions to large codebases
- Proficiency with one or more deep learning frameworks, such as TensorFlow, PyTorch, or JAX
- Strong understanding of machine learning principles, algorithms, and best practices
- Experience with cloud computing platforms (e.g., AWS, GCP, Azure)
Nice To Haves
- Master's or greater in computer science, machine learning, artificial intelligence, data science, or a related field
- Proven experience building and maintaining scalable MLOps pipelines.
- Experience working with IoT device data or embedded systems
- Familiarity with containerization and orchestration technologies like Docker and Kubernetes.
Responsibilities
- Design and implement scalable ETL and data preprocessing pipelines to handle large volumes of time-series, image, and sensor data from a range of sources
- Ensure data quality, integrity, and availability for model training and validation
- Develop strategies for data augmentation, normalization, and feature engineering
- Train, fine-tune, and rigorously evaluate a wide range of deep learning models
- Define and track key performance metrics (e.g. accuracy, MAE, F1) and relevant business-level KPIs
- Adopt version control for data, code, and models to maintain reproducibility and traceability of experimental results
- Clearly and effectively communicate technical details and limitations, experimental results, and strategic recommendations to a diverse audience, including marketing stakeholders, partnering engineers, and senior leaders
- Collaborate with cross-functional teams, including hardware, firmware, and cloud services, to ensure successful model integration and deployment
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Number of Employees
5,001-10,000 employees
