Grammarly team members in this role must be based in the United States or Canada, and they must be able to collaborate in person 2 weeks per quarter, traveling if necessary to the hub(s) where the team is based.

The opportunity 

Every day, tens of millions of people and 50,000 professional teams rely on Grammarly’s AI-enabled communication assistance to help them communicate confidently and achieve their goals. Our team members have the autonomy to take on exciting challenges in pursuit of our mission to improve lives by improving communication. Together, we’re building on more than a decade of steady growth and profitability. We’re defining the communication assistance category for individuals, enterprises, and developers with tailored service offerings: Grammarly Premium, Grammarly Business, Grammarly for Education, and Grammarly for Developers. All of this begins with our team collaborating in an inclusive, values-driven, and learning-oriented environment.

To achieve our ambitious goals, we’re looking for a Software Engineer, Machine Learning to join our Consumer Growth engineering organization. This person will shape and lead novel work to build advanced ML capabilities into the Grammarly Consumer Growth organization.

Grammarly’s engineers and researchers have the freedom to innovate and uncover breakthroughs—and, in turn, influence our product roadmap. The complexity of our technical challenges is growing rapidly as we scale our interfaces, algorithms, and infrastructure. Read more about our stack or hear from our team on our technical blog.

Your impact

As a Machine Learning Engineer, you will:

• Design and implement machine learning algorithms to solve business challenges related to growth, such as user acquisition, retention, and conversion.
• Collaborate with cross-functional teams such as product, design, data science, and engineering to understand business objectives and to develop data-driven solutions.
• Create experiments to test and validate the effectiveness of machine learning models.
• Build and maintain scalable infrastructure to support the deployment of machine learning models in production.
• Analyze and interpret model results to make data-driven decisions.
• Stay up-to-date with the latest machine learning and growth-related technologies and methodologies.
• Communicate technical concepts and results to both technical and non-technical stakeholders.

Your ramp-up plan

• Within the first 30 days, you will get to know everyone on the team. You will familiarize yourself with Grammarly and our projects, and gain a high-level understanding of our data processing, model training, and model serving workflows. You will begin executing small tasks from the team's backlog
• By month 3, you will take on medium-sized initiatives with a good level of autonomy to solve paid marketing spend. You will also help hire another ML engineer. 
• By month 6, you will be able to take on most tasks. You will work with Product Management and Marketing on key personalization and optimisation efforts, and you will expand beyond paid marketing and into lifecycle opportunities.
• By month 9, you will be able to drive an impact on both lifecycle personalization and have a tangible impact on paid spend.
• By year one, you will make a medium-sized impact on both lifecycle and paid acquisition and after a year, you may potentially build a long-term ML strategy for lifecycle and paid acquisition.

We’re looking for someone who

• Embodies our EAGER values—is ethical, adaptable, gritty, empathetic, and remarkable.
• Is able to collaborate in person 2 weeks per quarter, traveling if necessary to the hub where the team is based.
• Is independent, proactive, strategic, innovative, and data-driven.
• Has skills in feature engineering, system design, and code fluency.
• Possess strong knowledge of machine learning and computer science.
• Has strong written and verbal communication skills. 
• Understands recommendation systems and experimentation concepts (nice-to-have). 

Support for you, professionally and personally

• Professional growth: We believe that autonomy and trust are key to empowering our team members to do their best, most innovative work in a way that aligns with their interests, talents, and well-being. We support professional development and advancement with training, coaching, and regular feedback.
• A connected team: Grammarly builds a product that helps people connect, and we apply this mindset to our own team. Our remote-first hybrid model enables a highly collaborative culture supported by our EAGER (ethical, adaptable, gritty, empathetic, and remarkable) values. We work to foster belonging among team members in a variety of ways. This includes our employee resource groups, Grammarly Circles, which promote connection among those with shared identities, such as BIPOC and LGBTQIA+ team members, women, and parents. We also celebrate our colleagues and accomplishments with global, local, and team-specific programs. 

Compensation and benefits

Grammarly offers all team members competitive pay along with a benefits package encompassing the following and more: 

• Excellent health care (including a wide range of medical, dental, vision, mental health, and fertility benefits)
• Disability and life insurance options
• 401(k) and RRSP matching 
• Paid parental leave
• Twenty days of paid time off per year, eleven days of paid holidays per year, and unlimited sick days 
• Home office stipends
• Caregiver and pet care stipends
• Wellness stipends
• Admission discounts
• Learning and development opportunities

Grammarly takes a market-based approach to compensation, which means base pay may vary depending on your location. Our US and Canada locations are categorized into compensation zones based on each geographic region’s cost of labor index. For more information about our compensation zones, please refer to this page. If a location of interest is not listed, please speak with a recruiter for additional information. 

Base pay may vary considerably depending on job-related knowledge, skills, and experience. The expected salary ranges for this position are outlined below by compensation zone and may be modified in the future.