Senior software engineer at Instagram Machine Learning (IGML), a platform responsible for sourcing and recommending all Instagram related content

• Led a project to track and manage demand within IGML. This included implementing distributed tracing for client cost attribution, aggregating metrics across shared services, and creating a standard system for regression detection and admission control. Also delivered team wide keynote and workshops on managing resources solving future regressions, and best practices

• Led efforts to map the data lineage of IGML’s ecosystem into a knowledge graph then delivered a stakeholder keynote on how to leverage it for better dependency management, privacy control, and cost benefit analysis

• Led a project to restrict access and enforce deletion of personally identifiable information

• Rearchitected IGML client logging and realtime processing services into a more efficient layer that reduced host footprint by 25%

• Cut realtime feature storage footprint by 20% by deleting features with low model significance

• Optimized feature storage serialization and batched key records based on access pattern to reduce footprint by 30% and cache latency by 15%

• Migrated ML features to new framework which allowed the deprecation of a legacy feature framework- saving 75% of database write QPS, 10% of fleet size, and increased engagement by 0.7%

Modsy allows you to visualize your designs and furniture in your home before you buy it using realistic 3D renderings.

• Built and scaled a custom, in-house 3D render job pipeline

• Saved customer success 20 hours/week by creating an order management system with automated product refunding

• Improved query times by 50% and unified data access patterns by creating a data access layer for products and 3D render jobs

• Refactored and removed 20,000 lines of code within the first 3 months

• Increased unit test coverage from 58% to 80% by standardizing good coding practices and review

• Improved site reliability after benchmarking and refactoring expensive APIs and queries

• Created a release tool to automate deploys and release note creation that decreased deploy time by 5 hours/week

• Created an asynchronous job to parse and convert 3D asset files into V-Ray renderable files

• Provided real-time support for critical bugs using Bugsnag, Papertrail, Fullstory, and CloudWatch

Captricity is a management platform that captures and converts data generated by paper forms into business-ready information with an industry leading accuracy rate of 99.9%+.

• Increased workforce throughput by using Celery to asynchronously batch Amazon Human Intelligence Tasks (HITs) based on perceived effort, task type, and completion time

• Decreased job latency by building near real-time repricing of undesirable Amazon Human Intelligence Tasks (HITs)

• Designed and created a new regex model to support forms with pages that are optional, repeating, and out of order

• Increased system reliability by helping migrate a part of job pipeline into a separate microservice

• Decreased system downtime through throttling and splitting of large jobs that consumed excessive resources

• Improved customer turnaround time using job prioritization based on service level agreement (SLA)

• Ensured system scalability via benchmarking, optimizing Postgresql queries, and segregating Celery infrastructure

• Provided real time support during system outages using Airbrake, Splunk, NewRelic, and custom monitoring integrations

• Mentored two summer full-stack interns and helped them deliver core improvements on form setup and configuration

Natero is a customer success platform that uses machine learning to predict user behavior and big data analytics for deep customer insight.

• Built a JSON configuration compiler to chain Hadoop MapReduce jobs and perform cohort analysis on large datasets

• Implemented client retention rate and "layer cake" plot visualizations using d3

• Created a custom LRU Redis cache with locking for parallel operations and manual garbage collection