AI Scientist
Xaira Therapeutics
PyTorch
Staff Machine Learning Engineer
hims & hers
full-time
Posted on:
Location: 🇺🇸 United States
Visit company websiteSalary
💰 $210,000 - $230,000 per year
Job Level
Lead
Tech Stack
AWSPythonPyTorch
About the role
- How can we use data to build systems that enable access to significantly better health care? In this role as Staff Machine Learning Engineer, you will play a technical leadership role in leading the development and deployment of machine learning models that drive care personalization, treatment recommendations, and automation across our platform.
- Your work will directly impact strategic components of MedMatch, our AI-powered system that enhances provider-patient interactions, optimizes treatment recommendations, and expands into new verticals.
- Lead and contribute to the design and deployment of ML and LLM models for recommendation systems and personalized treatment strategies
- Work extensively with Python and ML libraries like PyTorch to scale and refine complex models
- Write high-quality, scalable, and production-ready code to support advanced ML applications
- Collaborate with engineers and product managers to deliver ML models integrated into real-world systems
- Design, scale, and improve ML infrastructure components to accelerate model training, evaluation, and deployment
- Research and integrate cutting-edge ML tools and frameworks, keeping the company at the forefront of ML and LLM advancements
- Mentor engineers within and across squads, sharing expertise in ML modeling, deployment, and best practices
- Drive improvements in team process and foster cross-team collaboration
Requirements
- 5+ years of experience in Machine Learning and/or Engineering, with a deep focus on hands-on model development and deployment
- Expert-level proficiency in Python and deep experience in ML frameworks like PyTorch
- Proven success leading ML model development and deployment efforts across multiple projects
- Strong background in LLMs and NLP applications, including applications like summarization and chatbots
- Proficiency in ML deployment best practices, with the ability to bring models from research to production
- Experience with ML infrastructure tools (e.g., Databricks, MLFlow, AWS SageMaker) is a plus
- A Master’s degree in Computer Science, Machine Learning, or a related field (not strictly required)
- A collaborative mindset, strong problem-solving skills, and the ability to influence direction across teams and technical areas
