Role Description As a Staff ML Engineer, you will be a hands-on technical leader within the Data Science organization, sharing your experience and establishing best practices. You will: - Design, implement, and deploy reliable ML models into production. - Build scalable data pipelines. - Develop both LLM-powered systems and multi-agent architectures to automate and accelerate cybersecurity risk assessment workflows. - Collaborate with cross-functional teams to integrate ML and LLM powered solutions into products. - Conduct research to stay ahead of emerging technologies. - 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. Qualifications - Bachelor’s degree in Computer Science, Engineering, Mathematics, Physics, or a related field. - 7+ years of experience or equivalent demonstrable skills in ML Engineering, Data Science or related discipline. - Proven track record as a technical lead, with the ability to guide teams, establish best practices, and drive technical strategy in collaborative environments. - Strong programming skills in Python, with hands-on experience using ML frameworks such as PyTorch, TensorFlow, and Scikit-learn. - Proficiency in data manipulation, cleaning and analysis using tools such as Polars, Pandas, NumPy, or SQL. - Extensive experience in traditional machine learning and data science tasks, including feature engineering, model selection, evaluation, and hyperparameter tuning. - 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 and LlamaIndex. - Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). Requirements - Master’s or PhD degree in Computer Science, Engineering, Mathematics, Physics, or a related field. - Experience with big data technologies such as Hadoop, Spark, or Kafka. - Experience implementing MLOps practices, including CI/CD pipelines, infrastructure as code with Terraform, and model versioning tools such as MLflow and DVC. - Proficient in deploying and maintaining high-quality ML pipelines, with robust monitoring and alerting for data and model drift, automated retraining, reproducibility, observability, and production-grade reliability and compliance. Benefits - SecurityScorecard is committed to Equal Employment Opportunity and embraces diversity. - We believe that our team is strengthened through hiring and retaining employees with diverse backgrounds, skill sets, ideas, and perspectives. - We make hiring decisions based on merit and do not discriminate based on race, color, religion, national origin, sex or gender (including pregnancy), gender identity or expression (including transgender status), sexual orientation, age, marital, veteran, disability status or any other protected category in accordance with applicable law. - We also consider qualified applicants regardless of criminal histories, in accordance with applicable law. - We are committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. - If you need assistance or accommodation due to a disability, please contact talentacquisitionoperations@securityscorecard.io. - Any information you submit to SecurityScorecard as part of your application will be processed in accordance with the Company’s privacy policy and applicable law. - SecurityScorecard does not accept unsolicited resumes from employment agencies. - Please note that we do not provide immigration sponsorship for this position.

AI Full Stack Engineer We’ve built an AI-native internal platform that powers every aspect of our Amazon brand management business. AI isn’t a feature — it’s the backbone. - LLMs classify and respond to inbound communications - AI generates pre-call intelligence briefs from raw enrichment data - A RAG system feeds context into every generation pipeline - An AI checkpoint system audits all generated content against quality gates The platform is already live and scaling fast: - 17+ background services - 130+ frontend pages - 214 backend services - 184 database tables - Dozens of autonomous AI pipelines We’re hiring an engineer who operates at the intersection of AI and production systems. You’ll build, optimize, and scale AI-powered infrastructure across the full stack. What You’ll Build & Scale AI Communication Pipelines - Classify inbound messages by category, intent, urgency, and tone - Generate contextual responses using enrichment data - Implement human approval gates AI-Powered Sales Intelligence - Transform raw enrichment data into structured pre-call briefs - Generate: background, pain hypotheses, talking points, rapport hooks RAG System - Vector database with embeddings - Markdown-aware chunking - Async ingestion workers - Semantic search API Trend Intelligence Engine - Process RSS feeds, social media, video platforms, and search trends - Generate reports, forecasts, and content drafts - Run autonomously on scheduled jobs Content Quality Pipeline - Multi-agent system (outline → audit → generate) - Binary quality gates (PASS/FAIL with citations) - Supports multiple content formats Automated Lead Qualification - Enrich leads with product data and market insights - AI scoring and qualification grading - Automated audit reports AI Executive Assistant - Slack operations - Scheduling workflows - Email triage and follow-ups

• Architect and develop an agentic AI platform. • Use this platform to implement the initial agentic workflows capable of automating complex processes in areas including Finance, HR, Legal, Customer Support, and Operations. • Mentor more junior agentic AI developers to further expand such flows. • Work with a variety of AI tools and platforms (e.g., Claude, AWS QuickSight, OpenAI, n8n, LangChain, etc.) to build seamless, integrated solutions. • Develop and deploy end-to-end solutions that include data ingestion, processing, decision-making, and action execution. • Fine-tune and customize AI models to meet specific business requirements, ensuring high accuracy and reliability. • Collaborate with cross-functional teams, including data scientists, engineers, and business stakeholders, to understand requirements and deliver impactful solutions. • Continuously monitor, evaluate, and optimize the performance of agentic systems to ensure scalability, efficiency, and robustness. • Maintain clear documentation of system architecture, workflows, and processes. • Provide regular updates and reports to stakeholders on system performance and outcomes. • Stay updated on the latest advancements in AI, machine learning, and automation technologies, and proactively identify opportunities to enhance existing systems.

• RAG pipeline & AI: Architect and develop the server-side RAG pipeline, covering query processing, embeddings and vector search through to prompt construction. • Full-stack development: Build both frontend and backend, ensuring that all LLM API calls are strictly handled server-side. • Subscriptions & payments: Implement a three-tier subscription model including a 7-day trial using Stripe. • Automation (Data Ingestion): Create a pipeline (e.g., using Make.com or n8n) that automatically transcribes new audio and video content (Whisper API), cleans it, splits it into chunks, and loads it into the vector database. • Data Intelligence: Develop an internal behavioral analytics dashboard that tracks search queries, emotional tone, content gaps, and usage-limit events in real time.