Senior Machine Learning Engineer

Mango - Los Angeles, CA

Full-time - Senior

Los Angeles, CA

11-50 employees

Merchant Wholesalers, Nondurable Goods

About the position

The Senior Machine Learning Engineer at Mango, Inc. is responsible for architecting, training, and evaluating neural networks to analyze microbial colony growth. This role involves collaborating with cross-functional teams to integrate machine learning models into data pipelines and imaging systems, while also optimizing training processes and staying updated on advancements in machine learning.

Responsibilities

• Architect, train, and evaluate neural networks for analysis of microbial colony growth.
• Analyze data quality, help clean data, and champion workflow improvements that enhance the value of data produced by the biology team.
• Collaborate with cross-functional teams, including software engineers, microbiologists, and hardware engineers, to integrate machine learning models into our data pipeline and proprietary imaging system.
• Analyze results and identify areas of improvement for model performance, working closely with the biology team to understand the nuances of the data.
• Lead efforts in optimizing the training process, including data augmentation and preprocessing, to enhance model generalizability.
• Keep abreast of state-of-the-art machine learning advancements and bring relevant techniques into our system.

Requirements

• 5+ years of experience in machine learning, with a focus on computer vision and convolutional neural networks.
• Proficiency in Python and experience with PyTorch.
• Strong ML intuitions and familiarity with common techniques and architectures.
• Background in image processing, ideally with experience in microscopy or other scientific imaging.
• Experience in developing models for noisy or complex real-world data.
• Passion for working in a collaborative, interdisciplinary environment and applying machine learning to solve biological challenges.

Nice-to-haves

• Experience with biological data, especially time-lapse microscopy or microbial imaging.
• Familiarity with incubator workflows and microbial growth analysis.
• Understanding of embedded systems or edge computing.