Machine Learning Engineer

About the role

Lovelace is the only provider of enterprise-scale context engines capable of analyzing trillions of real-time data points to create knowledge graphs that are usable by autonomous agents at the speed, scale, and accuracy required for mission-critical analysis. Lovelace's context engine platform, Elemental, uniquely integrates data ingestion, entity resolution, and graph building into a single pipeline that empower agentic deployments, delivering 1000X the investigative power for complex queries. With its proprietary ground-breaking YottaGraph, Lovelace provides enterprises with real-time, real-world context, enabling agents to understand the impact of global intelligence on enterprise data for unmatched insights with millisecond precision. Founded in 2023 by Andrew Moore, former head of Google Cloud AI, dean of Carnegie Mellon's School of Computer Science, and first AI advisor for U.S. CENTCOM, Lovelace currently works with some of the largest public and private enterprises in the world. Job Summary: As a Machine Learning Engineer, you will play a pivotal role in developing and deploying machine learning models and algorithms to address complex challenges in national security and emergency management. You will both learn a lot and teach a lot as we deal with some of the trickiest problems in the active area between large deep models and fine grained statistical inference.

Responsibilities

• Algorithm Development: Design, develop, and optimize machine learning algorithms and models for various applications, such as threat detection, image recognition, natural language processing, and predictive analytics.
• Efficiency and real-time operations: Work with colleagues to use every tool in the toolboxes of: (1) algorithm design (2) GPU-based optimization and (3) highly performance methodologies such as JAX, XLA, PyTorch.
• Model Training and Evaluation: Train, fine-tune, and evaluate machine learning models using appropriate frameworks and tools. Make sure that adaptive systems have hygienic and effective ML Ops.
• Deployment and Integration: Implement ML models into operational systems, ensuring seamless integration with existing infrastructure and applications.
• Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, domain experts, and government agencies, to develop and implement comprehensive ML solutions.
• Security and Compliance: Ensure that all ML solutions meet the highest security and compliance standards, especially when dealing with sensitive data and national security concerns.
• Documentation: Create and maintain detailed documentation of machine learning models, code, and processes to facilitate knowledge sharing and future enhancements.
• Testing and Validation: Conduct rigorous testing and validation of ML systems to ensure robustness, reliability, and accuracy under various conditions.

Requirements

• Bachelor's degree in Computer Science, Machine Learning, Data Science, or a related field (Master's or Ph.D. preferred).
• Proven experience in machine learning model development, training, and deployment.
• Proficiency in software development in familiar ML environments and a willingness to contribute to some new next-gen platforms.
• Enthusiasm for analytic methods from fields such as probability theory, statistics, linear algebra and knowledge graphs..
• Familiarity with cloud computing platforms (e.g., AWS, Azure) and distributed computing frameworks.
• Excellent problem-solving and analytical skills.
• Effective communication skills and the ability to work collaboratively in a team environment.
• Must be a US Citizen.
• Preferred Skills:
• Experience with deep learning and neural networks.
• Knowledge of geospatial data analysis and GIS tools.
• Understanding of ethical and legal considerations in AI and ML.

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