Staff Machine Learning Engineer

**About SecurityScorecard**: SecurityScorecard is the global leader in cybersecurity ratings, with over 12 million companies continuously rated, operating in 64 countries. Founded in 2013 by security and risk experts Dr. Alex Yampolskiy and Sam Kassoumeh and funded by world-class investors, SecurityScorecard's patented rating technology is used by over 25,000 organizations for self-monitoring, third-party risk management, board reporting, and cyber insurance underwriting; making all organizations more resilient by allowing them to easily find and fix cybersecurity risks across their digital footprint. Headquartered in New York City, our culture has been recognized by Inc Magazine as a "Best Workplace," by Crain's NY as a "Best Places to Work in NYC," and as one of the 10 hottest SaaS startups in New York for two years in a row. Most recently, SecurityScorecard was named to Fast Company's annual list of the World's Most Innovative Companies for 2023 and to the Achievers 50 Most Engaged Workplaces in 2023 award recognizing "forward-thinking employers for their unwavering commitment to employee engagement." SecurityScorecard is proud to be funded by world-class investors including Silver Lake Waterman, Moody's, Sequoia Capital, GV and Riverwood Capital. **About the Team**: At SecurityScorecard, the Data Science organization builds AI and ML products that empower our customers to manage cybersecurity risk. We leverage massive datasets sourced by our internal Threat Intelligence teams to create the core rating models that our customers use for assessing third-party risk and self-assessment. We also build LLM-powered systems for automating and accelerating cybersecurity risk assessment workflows. **About the Role**: As a Staff ML Engineer, you will be a technical leader within the Data Science organization, sharing your experience and setting best practices. You will design and implement ML algorithms, build scalable data pipelines, and deploy reliable models into production environments. You'll collaborate with cross-functional teams to integrate ML solutions into products, conduct research to stay ahead of emerging technologies, and ensure models perform optimally through ongoing monitoring and refinement. Your work will directly enhance cybersecurity resilience for organizations worldwide, making the world a safer place. If you're passionate about solving complex problems and creating impactful solutions, this role offers the opportunity to make a significant impact while working in a dynamic, collaborative environment. **Responsibilities**: - **Technical Leadership**: Establish best practices and share expertise through mentorship. - **Model Development**: Design, train, and optimize machine learning models and algorithms. - **Data Pipeline Creation**: Build and maintain scalable data pipelines to preprocess, clean, and transform raw data for analysis and model training. - **Model Deployment**: Implement and manage models in production environments, ensuring scalability, reliability, and performance. - **Research and Experimentation**: Stay updated on the latest machine learning techniques, tools, and frameworks to enhance model accuracy and efficiency. - **Collaboration**: Work closely with data scientists, software engineers, and product teams to understand requirements and integrate ML solutions into products. - **Performance Monitoring**: Continuously monitor, evaluate, and fine-tune models post-deployment to maintain accuracy and robustness. - **Documentation**:Create clear and concise documentation for models, processes, and systems to support team collaboration and knowledge sharing. **Required Qualifications**: - 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. **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**: Specific to each country, we offer a competitive salary, stock options, Health benefits, and unlimited PTO, parental leave, tuition reimbursements, and much more The estimated total compensation range for this position is $75,000 - $90,000 (USD base plus bonus). Actual compensation for the posit