Senior 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 Role

As a Senior ML Engineer, you will be a significant contributor within the Data Science & AI organization, sharing your experience and helping develop best practices. You will design, implement, and deploy reliable AI agents and ML models into production, build scalable data pipelines, and develop both LLM-powered systems and multi-agent architectures to automate and accelerate cybersecurity risk assessment workflows. You'll collaborate with cross-functional teams to integrate ML and LLM-powered 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

• Model Development & Deployment: Design and optimize LLM-based models, algorithms, and agents, then deploy them into production environments with a focus on scalability, reliability, and performance.
• LLM & Multi-Agent Systems: Develop and maintain advanced LLM-powered systems and multi-agent architectures to automate and accelerate cybersecurity risk assessment workflows. This includes designing conversational AI agents, orchestrating interactions between multiple agents, and building and integrating scalable RESTful APIs and microservices to expose model capabilities for integration with broader product ecosystems.
• Performance Monitoring: Supporting observability and evaluation of LLM-based agents to ensure long-term model accuracy, robustness, and stability.
• Data Pipeline Creation: Build and maintain scalable data pipelines to preprocess, clean, and transform raw data for analysis and model training.
• Research and Experimentation: Stay updated on the latest agent, LLM, and ML techniques, tools, and frameworks to enhance model accuracy and efficiency.
• Collaboration: Work closely with data scientists, ML engineers, software engineers, and product teams to understand requirements and integrate ML solutions into products.
• Documentation: Create clear and concise documentation for models, processes, and systems to support team collaboration and knowledge sharing.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Mathematics, Physics, or a related field.
• 4+ years of experience or equivalent demonstrable skills in ML Engineering, Data Science, or related discipline.
• Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Proficiency in data manipulation, cleaning, and analysis using tools such as Polars, Pandas, NumPy, or SQL.
• Solid understanding of supervised and unsupervised learning techniques, statistical analysis, hypothesis testing, and predictive modeling.
• Hands-on experience building multi-agent systems with large language models (LLMs) and retrieval-augmented generation (RAG) using tools like LangChain.

Preferred Qualifications

• Master’s degree in Computer Science, Engineering, Mathematics, Physics, or a related field.
• Experience with big data technologies such as PySpark and Kafka
• Experience implementing MLOps practices, including CI/CD pipelines and infrastructure as code with Terraform.
• Proficient in deploying and maintaining high-quality agentic solutions, with robust monitoring and alerting, observability, and production-grade reliability and compliance.

Benefits:

The estimated total compensation range for this position is $75,000 - $90,000 (base plus bonus). Actual compensation for the position is based on a variety of factors, including, but not limited to affordability, skills, qualifications and experience, and may vary from the range. In addition to base salary, employees may also be eligible for annual performance-based incentive compensation awards and equity, among other company benefits.