Machine Learning Engineer

About the role

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:

- As a Machine Learning Engineer, you'll own ML problems end to end, from framing an ambiguous question to shipping a production system. There's no ticket queue and no narrow scope: everyone here, including newer engineers, stands up new jobs and makes changes across the codebase without close supervision. You'll decide what data matters, which models fit, and how the system around them should work. The role is roughly half research and half engineering, on a team that reports to our Chief Scientist. At Lovelace, every engineer is a manager of AI. Our engineers work alongside AI coding agents every day, and the most valuable thing you'll bring is judgment: knowing what to build, catching when an agent gets it wrong, and owning the design decisions. We're looking for people who ramp in weeks, not months. You don't need industry experience; strong research lab backgrounds are welcome. What matters is that you've owned hard problems and can think critically about your own work and your agents'.

Key Responsibilities:

- Frame ambiguous product needs as precise ML problems, and set your own requirements.

- Decide what data is useful, which model types fit, and when to go beyond fine-tuning.

- Direct AI coding agents, review their output critically, and push back when their design decisions are wrong.

- Design the systems around your models: data pipelines, serving, evaluation, and monitoring.

- Choose metrics that reflect real outcomes, and catch when they mislead.

- Stand up new jobs, make changes across the codebase, and ship NLP and LLM systems to production, owning their correctness.

- Challenge assumptions, including teammates' and your own, to get to the right answer.

Qualifications:

- A track record of owning ambiguous problems end to end, in a research lab, a startup, or your own projects.

- Strong ML fundamentals, from probabilistic foundations to modern deep learning and LLM architectures.