Staff Machine Learning Engineer

Summary

Join SecurityScorecard as a Staff ML Engineer and become a technical leader in our Data Science organization. You will design and implement machine learning algorithms, build scalable data pipelines, and deploy reliable models into production. Collaborate with cross-functional teams to integrate ML solutions into products and conduct research to stay ahead of emerging technologies. Ensure optimal model performance through ongoing monitoring and refinement. Your work will directly enhance cybersecurity resilience globally. This role offers the opportunity to make a significant impact in a dynamic, collaborative environment.

Requirements

• 7+ years of experience or equivalent demonstrable skills in ML Engineering, Data Science or related discipline
• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Physics, or a related field
• Strong programming skills in Python
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn
• Proficiency in data manipulation and analysis using tools such as Polars, Pandas, NumPy, or SQL
• Solid understanding of algorithms, statistics, and data structures
• Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes)
• Knowledge of CI/CD pipelines and version control systems (e.g. Git)
• Familiarity with Linux/Unix command line tools

Responsibilities

• Establish best practices and share expertise through mentorship
• Design, train, and optimize machine learning models and algorithms
• Build and maintain scalable data pipelines to preprocess, clean, and transform raw data for analysis and model training
• Implement and manage models in production environments, ensuring scalability, reliability, and performance
• Stay updated on the latest machine learning techniques, tools, and frameworks to enhance model accuracy and efficiency
• Work closely with data scientists, software engineers, and product teams to understand requirements and integrate ML solutions into products
• Continuously monitor, evaluate, and fine-tune models post-deployment to maintain accuracy and robustness
• Create clear and concise documentation for models, processes, and systems to support team collaboration and knowledge sharing

Preferred Qualifications

• PhD degree in Computer Science, Engineering, Mathematics, Physics or a related field
• Hands-on experience with LLMs, RAG, LangChain, or LlamaIndex
• Experience with big data technologies such as Hadoop, Spark, or Kafka

Benefits

• Health benefits
• Unlimited PTO
• Parental leave
• Tuition reimbursements