Design, build, and operate scalable machine learning systems used in real-time targeting, decisioning, personalization systems
Own ML projects end-to-end, from data exploration and modeling to deployment, monitoring, and iteration
Proactively safeguard model and system quality through testing, monitoring, validation, and alerting
Collaborate cross-functionally with product, data, and other engineering partners to translate business problems into ML solutions
Continuously improve system reliability, performance, and engineering efficiency
Contribute to technical direction and best practices across the ML engineering team
Mentor and support other engineers through code reviews, design discussions, and knowledge sharing
Thrive in a high-impact, fast-paced, late-stage startup environment

Requirements

6+ years of professional experience building production machine-learning software systems
Proven experience owning ML systems long enough to see the downstream impact of design decisions
Strong proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or xgboost
Experience with data processing and analytics tools such as pandas, Spark, SQL, and matplotlib
Hands-on experience building automated pipelines for data processing, model training, validation, and deployment
Experience collaborating with cross-functional teams to deliver ML-powered features
Strong communication skills and a high sense of ownership.

Benefits

You'll get competitive perks and benefits, from health & wellness to equity, to help you bring your best self to work.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

machine learningPythonPyTorchTensorFlowxgboostdata processinganalyticsautomated pipelinesmodel trainingmodel validation

Soft skills

communicationownershipmentoringcollaborationproblem-solvingdesign discussionsknowledge sharingproactivityreliability improvementengineering efficiency