Responsibilities

 

As an early-stage startup, we are looking for team members who are ready and willing to take on a variety of system scale and architecture challenges as Plunk builds the most advanced, real-time residential real estate analytics platform:

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• Take accountability/ownership of all aspects of the Machine Learning lifecycle, including data cleaning, feature selection and engineering, training, inference, evaluation of quality, optimization of training and inference speed, and monitoring of deployed models 

• Apply multi-modal Deep Learning methodologies to utilize diverse data types in estimating property values 

• Design and execute experiments in feature engineering and model architecture to improve model performance and behavior of derivative analytics 

• Develop deep understanding of interaction between input data and model performance, including explainability of model outputs 

• Develop metrics for measuring consistency and performance of derivative analytics 

• Collaborate with a team to determine and implement automated methods for handling sparse, inconsistent data sources that assure robustness of machine learning models 

• Deliver presentations on technical details of algorithms and other analytics to customers and investors 

• Mentor team members and remain current with latest relevant research in the field 

 

Qualifications

 

• Graduate degree (PhD preferred) in Computer Science, Mathematics, Statistics or Physics or other field providing a rigorous mathematical and methodological grounding 

• 3+ years of demonstrated experience developing production machine learning-based solutions in multiple business contexts/domains 

• Demonstrated experience developing solutions with Deep Learning frameworks (preferably TensorFlow) 

• Highly proficient in solution development with Python 

• Proficient in SQL for data extraction and manipulation 

• Experience with AWS tools and frameworks 

• Ability and willingness to work in Agile/Scrum 

• Ability to work collaboratively through ambiguity, with urgency, patience, and good humor 

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Bonus points and other considerations:

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• Prior experience working at a tech startup

• Collaborate with product, engineering and design teams to conceive of and enable new products and customer features 

• Applied Math background is a plus 

• Demonstrated skills in Java 

• Experience with geospatial modeling and data 

• Experience with Computer Vision models 

• Experience implementing machine learning explainability techniques (i.e. SHAP, LIME)

• Knowledge of ML Ops and ability to research best practices and tools/solutions in the ML Ops space 

• Experience working with Snowflake 

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Benefits:​

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• Significant, early-startup Incentive Stock Option (ISO) Equity package

• The best Full medical, dental, and vision insurance plans available!

• Learning: class reimbursement, conference, and training sponsorships

• Unlimited PTO