Staff Machine Learning Engineer

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, 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:

• Technical Leadership: Establish best practices and share expertise through collaboration and mentorship.
• Model Development & Deployment: Design, train, fine-tune, and optimize machine learning models and algorithms, 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 scalable RESTful APIs and microservices to expose model capabilities for integration with broader product ecosystems.
• Performance Monitoring: Implement best practices such as continuous monitoring, data drift detection, and automated retraining 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 machine learning techniques, tools, and frameworks to enhance model accuracy and efficiency.

Required 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).