Machine Learning Operations Engineer

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Join Grammarly as a Machine Learning Operations Engineer

Embrace Remote Flexibility with Grammarly's Hybrid Working Model

Grammarly offers a modern work approach with its remote-first hybrid model. Being part of our team means you can work mainly from home in top countries such as the United States, Canada, Ukraine, Germany, or Poland with occasional in-person meetings. Our innovative model requires some roles to be located near Grammarly hubs to foster better collaboration.

Every quarter, we bring teams together for 2-4 weeks of collaboration in vibrant hubs like San Francisco, Kyiv, New York, Vancouver, Berlin, or Kraków. This strategy ensures that our team members enjoy focus time, as well as in-person interactions that enhance trust and creativity.

Be Part of a Pioneering Company in AI Writing Assistance

Grammarly leads the way in AI writing technology, assisting over 30 million people and 70,000 teams daily. Our products make significant impacts across the Fortune 500, facilitating clear and effective communication. With more than a decade of profitability, our adherence to core values and commitment to privacy has set us apart as a secure and reliable platform.

We are recognized as one of Inc.’s best workplaces and have earned accolades from Glassdoor, TIME, and Fast Company for our influence and innovation in AI.

Role Overview: Machine Learning Operations Engineer

We're looking for a talented Machine Learning Operations Engineer to join our Core Product team. This role focuses on the construction and enhancement of ML inference infrastructure to support large language models for millions of users. By collaborating with internal teams and optimizing machine learning models, you'll influence performance and improve our product offerings.

Your technical proficiency will be crucial in scaling our systems and refining our flagship premium features. This is a fantastic opportunity to push the limits of machine learning and substantially contribute to our innovative product suite.

Your Impact

As an ML Operations Engineer, your work will shape the future of our large-scale ML initiatives. From system design to integration, you will enable efficient and robust machine learning operations across our platforms. This includes:

  • Developing infrastructure for ML inference to enhance service delivery.
  • Optimizing machine learning models to achieve the best trade-offs among latency, cost, and quality.
  • Managing third-party LLM provider integrations to ensure optimal performance.
  • Creating a bridge for the continual feedback loop between platform and feature teams.
  • Investigating external technologies and tools to stay ahead in building state-of-the-art systems.

Who We're Looking For

The ideal candidate embodies Grammarly's EAGER values and MOVE principles, with a strong background in both traditional machine learning and deep learning technologies. We value individuals who are not just capable of technical excellence but are also great collaborators, ready to meet the unique challenges of NLP applications.

Comprehensive Benefits and a Supportive Environment

Grammarly offers competitive salaries and exceptional benefits, including comprehensive health coverage, wellness stipends, and learning opportunities. Our inclusive culture encourages connection and celebrates diversity, making Grammarly not just a workplace but a community.

How to Apply

Unlock your potential at a company that values diversity and innovation. Apply now to begin your journey with Grammarly. We are dedicated to equal opportunity and inclusivity, welcoming applicants of all backgrounds.

For more information on our data privacy practices during recruitment, please visit the Grammarly Data Privacy Notice for Candidates.

#LI-Hybrid

All Grammarly team members participating in in-person activities are encouraged to be vaccinated against COVID-19.

Additional Information:

  • Company: Grammarly
  • Job Title: Machine Learning Operations Engineer
  • Locations: Germany, Poland (Must be willing to travel to hubs as required)