Senior Machine Learning Engineer

Summary

Join SecurityScorecard as a Senior 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. Collaborate with cross-functional teams to integrate ML solutions into products and conduct research to stay ahead of emerging technologies. This role offers the opportunity to make a significant impact on cybersecurity resilience while working in a dynamic and collaborative environment. You will mentor junior engineers and establish best practices. Your work will directly enhance cybersecurity resilience for organizations worldwide.

Requirements

• 5+ 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

• Competitive salary
• Stock options
• Health benefits
• Unlimited PTO
• Parental leave
• Tuition reimbursements
• Annual performance-based incentive compensation awards
• Equity