Own ML platform architecture: data/feature pipelines, experiment tracking, model registries, serving layers, offline/online evaluation, and observability
Define standards for reliability, performance, cost efficiency, security, governance, and model risk management across ML services
Lead design and implementation of models across classical ML and deep learning
Build scalable training and inference pipelines; establish CI/CD for ML
Implement monitoring for data quality, drift, fairness, latency, reliability, and cost
Mentor engineers and junior tech leads; codify best practices

Requirements

Bachelor’s degree or above in Computer Science, Engineering, Statistics, or related field
10+ years of professional software development experience using at least two general-purpose languages (e.g., Java, C++, Python, C#)
10+ years architecting, designing, and building multi-component ML platforms leveraging open-source/cloud-agnostic components
6+ years managing end-to-end SDLC for ML systems
6+ years working with cloud providers (Azure and/or AWS) in production ML contexts

Benefits

Market-competitive compensation
401(k) savings plan vested from day one with 6% match
Performance and recognition-based incentives
Tuition assistance
Mental healthcare support
Fertility and adoption assistance
Flexibility to work from anywhere in the US for up to four weeks per year

Applicant Tracking System Keywords

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

Hard skills

machine learningdeep learningdata pipelinesfeature pipelinesCI/CDmodel registriesexperiment trackingscalable traininginference pipelinessoftware development

Soft skills

mentoringleadershipbest practices

Certifications

Bachelor’s degreeMaster’s degree