You will productionize models by converting research-grade code into performant, clean, and maintainable production systems.
You will implement MLOps best practices, including CI/CD for machine learning, automated retraining pipelines, and robust model versioning.
You will optimize models for inference to ensure high-speed performance and efficiency in real-time environments.
You will monitor models in production, proactively identifying and mitigating issues related to data drift, concept drift, and system latency.
You will identify and implement iterative improvements to the machine learning models that power production-scale, customer-facing experiences.
You will serve as a technical bridge, assisting other engineers and stakeholders in understanding and applying data science methodologies and findings across the organization.

5+ years of software engineering experience, with at least 2 years specifically focused on deploying and scaling machine learning models in production environments.
Highly proficient in Python and capable of writing production-grade, modular code.
Deep understanding of the end-to-end ML lifecycle, including training versus inference workflows, feature stores, and model versioning.
Competent with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
Hands-on experience with Docker and Kubernetes.
Proficient in SQL and familiar with distributed data processing tools like Spark or Kafka.

Competitive compensation packages with a salary, bonuses, and restricted stock grants.
Generous benefits, including paid vacation, medical, dental, and vision insurance, and paid family leave.
A high-growth company, providing opportunities for continued professional development and growth.

Senior Applied Machine Learning Engineer

Key skills