Machine Learning Research Engineer

Parisi Labs•Boston, MA

4d•$210,000 - $275,000•Hybrid

About The Position

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 (https://askthegrid.com) 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. 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.

Requirements

You have an MS, PhD, or equivalent demonstrated depth in machine learning, computer science, statistics, applied mathematics, electrical engineering, or a related field.
You can read a paper, implement the important idea, and determine whether it actually works.
You have strong Python and modern machine-learning framework experience.
You understand experimental design, statistical reasoning, and the many ways an ML result can be misleading.
You have worked with sequence models, probabilistic modeling, forecasting, scientific ML, optimization, or other learning problems grounded in real systems.
You have improved a real model under practical data, compute, or deployment constraints.
You write clear research code and communicate technical conclusions without hiding behind jargon.
You want substantial ownership and can operate without a large, mature research organization around you.

Nice To Haves

A particularly strong archetype is someone with a research-heavy graduate background followed by two or three years of applied industry work, but credentials are not a substitute for evidence of excellent work.

Responsibilities

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.