Top Software Engineer Jobs

The Manager, Analytics Engineering at Grammarly will lead, scale, mentor, and drive the Analytics Engineering team in Europe. Responsibilities include building an analytics engineering team, fostering a data-driven culture, empowering decision-makers, and owning data delivery for stakeholders.

Join Grammarly as a Software Engineer focusing on Windows application development. Contribute to the development of a product offering that delivers Grammarly's AI system to Windows users. Work on performance, quality, and integration with desktop applications. Collaborate with a team to innovate and solve technical challenges.

Join Grammarly as a Machine Learning Engineer specializing in NLP to build new products and capabilities for Enterprise customers. Collaborate with a high-performing team to leverage ML advances, linguistic expertise, and product design to improve communication in customer organizations.

Lead a high-performing team in building graphs of data for Enterprise customers at Grammarly. Develop a knowledge graph integrated with analysis systems using cutting-edge technologies like LLMs and graph databases. Collaborate with cross-functional teams to deliver industry-leading capabilities and advocate for responsible data use.

Join Grammarly's Responsible AI team as a Machine Learning Engineer specializing in NLP. Develop and implement new ML solutions to enhance the safety and fairness of Grammarly products. Work in cross-functional teams to leverage ML advances and build scalable solutions.

The Analytics Engineer at Grammarly will be responsible for enabling data-driven decision-making by building analytical solutions for product, marketing, and business leaders. They will work on data delivery, defining business metrics, designing dimensional tables, and creating scalable analytical solutions.

Senior DevOps Engineer role on the Data Platform team at Grammarly. Responsible for leading cloud infrastructure design, automation, and management, focusing on Databricks and AWS. Optimizing data processing and machine learning workflows for high performance and uptime. Collaborating with cross-functional teams to automate and optimize workflows, manage infrastructure, and support real-time analytics and machine learning at scale.