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B2B SaaS to help companies grow machine learning teams from hiring to productivity

Machine Learning Engineer

$140k – $180k • 0.8% – 1.4%
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About the role

Machine learning is core to our business, driving everything from our interview generation to our candidate ranking system. We're looking for a talented, experienced ML engineer to help us scale quickly, smartly and nimbly. This role offers a unique opportunity to join an amazing team and grow your career as AdaptiLab scales.


-Build machine learning products that help drive our business
-Make thoughtful architectural decisions with limited information translating business requirements into machine learning products
-Communicate decision rationales, formulating project plans and delivering results
-Automating model comparison and feature selection to optimize performance
-Shipping code to our staging and production environments multiple times a day
-Building data pipelines that allow us to improve our machine learning products and services
-Design and deploy efficient data services and stores

Who we're looking for:

-You have 3+ years experience leveraging machine learning to solve complex business problems
-You’re adaptable: You thrive in a fast-paced, ever-changing environment, and understand that the right solution and the perfect solution rarely coincide in startups
-Excited about AdaptiLab's mission of helping companies adopt Machine Learning technology
-Bonus: Masters or PhD in computer science or other STEM degree

You have some experience with:

-Python and SQL (Both required)
-Designing and deploying scalable production machine learning products
-Machine learning algorithms (Neural networks, gradient boosting) and model evaluation (cross validation, bias-variance trade-off)
-Architecting relational databases (PostgreSQL), NoSQL databases (MongoDB), and data warehouses
-Git and GitHub; software development workflows
-Managing deployment & operation of cloud services (AWS, Azure etc.)
-Big data tools (Kafka, Spark, HDFS)
-Bonus: Early-stage startup experience, especially joining at the seed or Series A stage and scaling a technical product

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