Who we are
Our mission at Nylas is to turn communication into inspiration and insight. We empower over 100,000 developers and over 900 companies all over the world to access, parse, and gain insights from communications data to provide better experiences for their customers and users, all while providing top tier security and compliance. To achieve this vision, we’ve raised over $175M from Tiger Global, 8VC, ScaleUp, Spark Capital, Slack, and more.
 
We have a huge market (every company that builds software) and a massive opportunity (the world runs on communication and data). By continuing to hire exceptional people from all different backgrounds and perspectives, we have the opportunity to make Nylas one of the biggest, most successful and equitable technology companies in the world. 
 
Why Nylas
Nylas is an advocate for the well-being of our employees. We trust our employees and give them the autonomy to achieve their goals without focusing on when, where, and how they get there. We are a flexibility first workplace, but if Nylanauts want to work from an office, we have hubs in San Francisco, Denver, New York City, Toronto, and London.
 
We are also strong supporters of internal and cross-team mobility and growth. We want Nylas to be a place where anyone can be supported to grow, learn, and become the best at what they do. We’ve been named a top Startup for career growth and development by Forbes, Great Places to Work, and Comparably!
 
From our CTO + Co-Founder, Christine Spang:
“We have a number of folks on the team who started as an individual contributor and became staff level or leaders in their area. I personally love seeing people reach their full potential and become successful at Nylas.”

The Role

As a Software Engineer in the Machine Learning team, you’ll help develop/maintain ML based solutions, from rule-based to deep learning models, that our customers will use to turn unstructured data into useful information. In this role, you will spend approximately 90% of your time on engineering/devops tasks and 10% ML specific tasks.

You should have a growth mindset, a track record of managing your own projects, and a good sense of practical shippability over engineering purity. You should also tend toward humbleness in your abilities and have an innate desire to pass knowledge onto others.

What You’ll Do

  • You’ll help build and maintain our microservices and REST APIs, which expose ML models
  • You’ll help re-evaluate the tradeoffs of already shipped features/ML systems and reduce technical debt
  • You’ll be working with ML engineers building an intelligence layer on top of email, calendars, and contacts
  • You’ll apply ML and engineering best practices to tests ML solutions
  • You’ll be using the latest and greatest tools to build things the way you want with little to no legacy code to stand in your way
  • You’ll be building out machine learning infrastructure for billions of datasets with trillions of connections

What You’ll Bring

  • Curious and passionate about solving customer problems
  • Background in Computer Science or Software Engineering
  • You love compiling complex RegExes
  • You’ve shipped ML models to production
  • At least 5 years of experience with Python, Scala, Golang (Go) or Java
  • Experience creating and developing on RESTful APIs
  • Understanding of Statistics, Data Modeling, and Machine Learning is preferred but if you are a solid software engineer, we are happy to train you 
  • Bonus if you have experience with natural language processing packages!
  • Comfortable with ambiguity and exploring ideas never done before
  • An attitude of continuous learning
  • Not afraid to change your opinion in the face of new information or understanding of the product goals—you have beliefs, but you’re open-minded too!
Not sure if this is you?
We want a diverse, global team, with a broad range of experience and perspectives. If this job sounds great, but you’re not sure if you qualify, apply anyway! We carefully consider every application and will either move forward with your application, find another team that might be better suited to your skills, keep in touch for future opportunities, or thank you for your time.

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