Senior Platform Engineer, Machine Learning
Fieldguide- Full Time
- Senior (5 to 8 years), Mid-level (3 to 4 years)
Candidates should possess 3-5 years of experience as an ML Engineer, preferably with a background in the Gaming industry, and demonstrate experience with ML frameworks such as TensorFlow, Scikit-Learn, and PyTorch. They should also have a strong background in MLOps practices, including CI/CD, containerization (Docker), orchestration frameworks (Kubernetes, Airflow), model serving tools (AWS SageMaker, Databricks MLFlow), model observability frameworks, automation, and feature stores.
The ML Engineer will develop large-scale distributed machine learning systems, collaborate with cross-functional teams to deploy and integrate machine learning models, liaise with business units to address their ML needs and contribute to the cross-BU ML portfolio, optimize feature extraction, transformation, and selection, work with and manage feature stores for reusability, ensure scalability, reliability, cost efficiency, and ease of use of the machine learning platform, and contribute to evaluating and adopting new technologies and tools to enhance machine learning capabilities.
Develops AI tools for sustainable agriculture
Mineral.ai develops technology solutions aimed at improving the agriculture industry. The company utilizes perception technology, artificial intelligence (AI), and machine learning (ML) to create tools that help farmers, researchers, and agricultural advisors increase crop yields, manage pests, and adapt to climate change. Their products include precision agriculture tools that optimize resource use and advanced data analytics platforms that provide insights from agricultural data. Unlike many competitors, Mineral.ai focuses on creating partnerships within the agriculture sector to co-develop solutions, enhancing their product offerings. The goal of Mineral.ai is to support sustainable food production and help feed the world more efficiently.