Compared with the selected benchmark ("All roles in Remote - Global"), this listing's salary midpoint is about 129% higher. The offer sits above the benchmark range (€2,484–€14,831). The listed pay band (€13,750–€25,833) is tighter than the benchmark, which suggests lower salary variability. This benchmark is based on 479 comparable listings.

Monthly salary comparison for Research Engineer
Market	Lower bound (25th percentile)	Median	Upper bound (75th percentile)
All roles in Remote - Global	€2,484/per month	€7,145/per month	€14,831/per month

About the role

Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction. Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in. We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.

Job Details

Responsibilities

Develop and post-train models, build systems to run, evaluate, debug, and scale training workloads.
Build software, tooling, and platform capabilities for researchers, developers, and customers.
Contribute to Lightning’s open-source projects by building new features and collaborating with the broader community.
Work across deep learning systems, developer tooling, backend services, and platform infrastructure.
Collaborate with customers to understand AI workloads and translate learnings into product improvements.
Prototype new ideas, evaluate approaches, and turn experiments into production-quality software.
Partner with research, product, and infrastructure teams to improve developer experience and AI workflows.
Debug complex technical problems spanning ML, distributed systems, and tooling.

Requirements

Experience building, training, evaluating, or experimenting with deep learning models.
Hands-on experience with deep learning frameworks such as PyTorch.
Strong software engineering fundamentals building software and debugging and problem-solving skills.
Curiosity, initiative, and a demonstrated ability to quickly learn new technologies and technical domains.