Lead and manage high-performing teams across ML engineering and ML infrastructure
Define and execute the strategic roadmap for ML systems
Oversee the design, development, and deployment of scalable ML pipelines
Architect distributed systems to support ML workloads at scale
Partner closely with Product, Engineering, and Content teams
Support best practices in experimentation, evaluation, and ML system monitoring
Ensure cost efficiency, scalability, and performance in ML infrastructure investments

Requirements

10+ years of industry experience in machine learning engineering and distributed systems
3+ years of leadership and management experience
MSc or Ph.D. in Computer Science, Machine Learning, or related field, or equivalent practical experience
Proven expertise in building and deploying end-to-end ML systems at scale
Strong background in distributed systems architecture
Hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch)
Track record of delivering high-quality, scalable, and fault-tolerant systems
Excellent communication skills

Benefits

Medical/dental/vision insurance
401(k) plan
Paid time off
Annual discretionary bonus
Long-term incentive plan
Flexible Time off Policy
Generous Parental Leave Program
Monthly wellness reimbursement

Applicant Tracking System Keywords

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

Hard Skills & Tools

machine learning engineeringdistributed systemsML pipelinesML systemsdeep learning frameworksTensorFlowPyTorchscalable systemsfault-tolerant systemscost efficiency

Soft Skills

leadershipmanagementcommunication

Certifications

MSc in Computer SciencePh.D. in Machine Learning