Machine Learning Research Engineer

Аналитика рынка

Откликнуться

Загрузите резюме — мы свяжем вас с работодателем напрямую через нашу базу.

Описание

About Parisi Labs Parisi Labs is an AI company building learning systems for complex physical environments. We combine historical and live data with real operational context to help people understand the present, evaluate possible futures, and make better decisions. Energy is our first proving ground. Ask The Grid ([link] is our public product for exploring the systems, markets, and assets that make up the power grid. We are a small technical team working across machine learning, data infrastructure, software, and real-world operations. About The Role We are looking for a machine learning research engineer to work directly with our Chief Scientist and accelerate our core modeling work. You will inherit a real model and evaluation system, understand how it behaves, and make it materially better. That means implementing ideas from papers, designing careful experiments, debugging training and data problems, improving evaluation, and translating successful research into reliable systems. This is neither a purely academic research position nor a conventional production-ML role. It is for someone who enjoys the full empirical loop: form a hypothesis, build the experiment, determine whether the result is real, and ship what works. What You Will Own - Reproduce, extend, and improve our model-training and evaluation systems. - Design experiments and ablations that separate meaningful improvements from noise, data problems, and evaluation artifacts. - Investigate model behavior through error analysis, diagnostics, and carefully constructed benchmarks. - Build better tooling for experimentation, tracking, reproducibility, and technical decision-making. - Work closely with data and product engineers to turn research requirements into dependable systems. - Translate promising research into production-quality implementations. - Communicate results clearly: what changed, what the evidence shows, and what we should try next. - Help establish the research practices and technical standards of an early AI company. First 90 Days - 30 days: Reproduce the current model and evaluation system, identify fragile assumptions, and ship an early improvement to the research workflow. - 60 days: Own an experiment from hypothesis through implementation, evaluation, and failure analysis. - 90 days: Run a dependable weekly research cadence with reproducible results, clear readouts, and evidence-backed recommendations. You May Be A Fit If - You have an MS, PhD, or equivalen

Контакты работодателя (email/phone/telegram) скрыты из публичного превью — отправьте резюме, чтобы мы связали вас напрямую.