hims & hers

Senior Staff Machine Learning Engineer

hims & hers

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Salary

💰 $240,000 - $260,000 per year

Job Level

Senior

Tech Stack

AWSPythonPyTorchTensorflow

About the role

  • How can we use data to build systems that enable access to significantly better health care? In this role as Sr. Staff Machine Learning Engineer, you will define and lead the architecture and development of large-scale, foundational machine learning systems that power care personalization, treatment intelligence, and automation at scale. Own and drive architectural decisions for ML and LLM models for treatment recommendation, personalization, and intelligent care delivery Work extensively with Python and ML libraries like PyTorch to build, optimize, and scale complex model pipelines Write and review high-quality, production-ready code supporting high-impact, cross-team ML systems Partner with engineers and product leadership to align ML development with strategic priorities Architect a robust ML infrastructure to enable streamlined training, evaluation, deployment, and monitoring Drive the adoption of new ML technologies and practices, contributing to long-term group-level technical maturity Mentor senior and staff-level engineers across multiple squads, guiding system-level thinking and best practices Champion a culture of scalable system design, engineering rigor, and customer-first innovation

Requirements

  • 8+ years of experience in Machine Learning and/or Engineering, with a track record of technical leadership and ML system architecture Expert-level proficiency in Python and advanced ML frameworks such as PyTorch, TensorFlow Demonstrated success in architecting and maintaining ML platforms at scale across teams or business units Deep experience in LLMs and NLP, including transformer models, summarization, and chatbot applications Advanced understanding of ML infrastructure, including continuous training, real-time inference, and operational reliability Experience with ML infrastructure tools (e.g., Databricks, MLFlow, AWS SageMaker) A Master’s degree or PhD in Computer Science, Machine Learning, or a related field (not strictly required) Exceptional communication and cross-org leadership skills, with the ability to align diverse stakeholders around ML strategy