Job description

About Us: 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: - The Staff AI Research Engineer will lead the design, development, and deployment of advanced AI models and systems that power Lovelace’s context engine and agentic platforms. Day-to-day responsibilities include researching and productionalizing ML models, pipelines, and services and working closely with product management and infrastructure engineers. In this role you will develop key components of the Elemental platform including multimodal data ingestion, entity resolution and knowledge graph construction, and agentic search. The Staff AI Research Engineer will mentor team members, collaborate cross-functionally with product and infrastructure groups, and contribute to externally facing research, technical documentation, and internal best practices. This is a full-time, on-site role based in Pittsburgh, PA. Qualifications: - Strong foundation in Machine Learning and AI Engineering, with experience building and scaling production-grade AI systems. - Graduate degree in Computer Science, Machine Learning, or a related field, or equivalent practical experience. - Proficiency in ML frameworks such as PyTorch or TensorFlow, training pipelines, and deploying and scaling models on Kubernetes-based infrastructure (e.g., GPU scheduling, model serving, and MLOps tooling). - Ability to design rigorous experiments, interpret results, and communicate technical findings clearly to both technical and non-technical stakeholders. Preferred Skills: - Advanced expertise in one or more of the following: Multimodal ML, NLP, Entity Resolution, Knowledge Graphs, Agentic Search. - PhD with published research works in major ML or NLP conferences (NeurIPS, ICML, ICLR, AAAI, EMNLP, etc.). - Experience in a technical leadership role that required mentoring, communicating technical findings to external stakeholders, and cross-functional project management. - Experience getting a product to market and working backwards from the customer to get to the right solution, balancing technical rigor with practical delivery. Benefits: LovelaceAI offers competitive compensation packages, comprehensive benefits. We provide a supportive and inclusive work environment where your skills and expertise can make a significant impact on the safety and security of our communities. Lovelace’s founding team includes: Andrew Moore, who has a track record of building impactful AI systems, designing them with human rights impact assessments as a top priority, leading the AI division of one of the world’s foremost cloud companies, and actively participating in machine learning and AI research over the past two decades. Toby Smith, well known in the Pittsburgh Tech community for his engineering leadership and design skills, and who has led many of the most ambitious and complex system infrastructure projects in Google Pittsburgh and NetApp. Here is a note from Andrew Moore to people who are reading these Job Postings: “Hi folks, I’m so glad you are potentially interested in Lovelace AI. This area means a lot to me because while I am an AI optimist, I also think that we technologists owe it to a rightly skeptical world to show that modern intelligent systems can actually be useful. Usefulness comes in many guises: from life sciences to education and from transportation to entertainment and many others. For many of us, security and public safety is also very high on that list. That reasoning leads to this conclusion: I’m determined to make sure that the people building Lovelace AI gain a lot from the experience, including the chance to solve fascinating problems in computer science, AI, business development, customer success and product management. I also hope that we all learn from each other in a highly enriching work environment. But my main hope is that we have a shared sense of accomplishment as we see an increasing number of national security and public safety domains made safer through sensible and robust use of advanced computer science."

Requirements

Must-haves

Strong foundation in Machine Learning and AI Engineering, with experience building and scaling production-grade AI systems.
Graduate degree in Computer Science, Machine Learning, or a related field, or equivalent practical experience.
Proficiency in ML frameworks such as PyTorch or TensorFlow, training pipelines, and deploying and scaling models on Kubernetes-based infrastructure (e.g., GPU scheduling, model serving, and MLOps tooling).
Ability to design rigorous experiments, interpret results, and communicate technical findings clearly to both technical and non-technical stakeholders.
Experience in a technical leadership role that required mentoring, communicating technical findings to external stakeholders, and cross-functional project management.
Here is a note from Andrew Moore to people who are reading these Job Postings:

Nice-to-haves

Advanced expertise in one or more of the following: Multimodal ML, NLP, Entity Resolution, Knowledge Graphs, Agentic Search.
PhD with published research works in major ML or NLP conferences (NeurIPS, ICML, ICLR, AAAI, EMNLP, etc.).
Experience getting a product to market and working backwards from the customer to get to the right solution, balancing technical rigor with practical delivery.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningkubernetes

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Vision