LoopSmart is a technology research and development company. We build advanced software, AI systems, and infrastructure that power our partners' products. We focus on creating tangible technology solutions that solve complex problems at scale.

We are seeking a Research Engineer to bridge the gap between theoretical research and practical application. In this role, you will turn ideas into evidence: designing experiments, measuring outcomes, and translating results into engineering artifacts. You will value reproducibility, careful reasoning, and pragmatic execution over novelty for novelty's sake.

You will be responsible for the full lifecycle of our research projects: from formulating hypotheses and designing experiments to building the prototypes that demonstrate their value. You will work to ensure that our research is not just interesting, but useful and deployable.

Key Job Responsibilities

• Design experiments and define success metrics before writing large amounts of code.
• Build prototypes that can evolve into production components (clean interfaces, tests, and documentation).
• Run ablations and error analyses; communicate findings in a clear, audit-friendly way.
• Partner with engineering to integrate outcomes into systems, pipelines, or operational processes.
• Investigate practical questions: how to improve robustness, reduce brittleness, and make outcomes more predictable.
• Stay up-to-date with the latest research and identify opportunities for application.

A day in the life

Your day might start by reading a new paper on efficient fine-tuning methods. You formulate a hypothesis about how this could improve our domain-specific models and design a small experiment to test it. Later, you write a script to generate a synthetic dataset for evaluation, ensuring it covers edge cases relevant to our partners. In the afternoon, you analyze the results of a previous experiment, finding that a simpler approach actually outperforms the complex one. You write up your findings in a clear technical report, recommending the simpler path. You wrap up by refactoring your experimental code so it can be reused by others.

About the team

You will join a small, high-density team of researchers and engineers who value shipping over hype. We operate like a lab: we form hypotheses, run experiments, and document results. We are not a feature factory; we are an asset factory. We value clear writing, intellectual honesty, and the discipline to finish what we start. We work asynchronously and respect deep work time.

Basic Qualifications

• 3+ years of experience in applied R&D or software engineering.
• Ability to write production-quality code and to reason statistically about experiments.
• Experience with measurement and iteration (benchmarks, evaluation suites, regression testing).
• Strong communication: you can explain trade-offs, assumptions, and limitations.
• Bachelor's degree in Computer Science, Math, or related field.

Preferred Qualifications

• Master's degree or PhD in Computer Science, AI, or a related field.
• Experience in applied ML/AI, information retrieval, or evaluation methodology.
• Background in reliability engineering (monitoring, failure modes).
• Comfort writing clear technical reports and maintaining internal documentation.