Machine Learning Engineer
San Francisco, CA
BuildZoom is fixing the trillion-dollar construction industry by providing people with the information they need to make great decisions and tools needed to simplify and de-risk the process from start-to-finish. As a full-stack engineer with BuildZoom, you'll play an incredibly important role by building technology to transform an offline process fraught with confusion and complexity into one that is transparent and simple.
- Build elegant solutions to the workflow challenges associated with home-remodeling projects
- Brainstorm and implement creative ways to visualize hundreds of millions of complex data points
- Overcome the challenges associated competing priorities: beautiful aesthetic, UX, page performance and SEO
- Deploy new features daily and iterate on improvements
- Give and receive constructive code reviews
You should have:
- At least 3 years professional experience
- At least a bachelors degree in Computer Science or related subject
- Experience with medium-to-large scale RoR deployments
- Strong fundamentals: OOP and application architecture
- Appreciation for software craftsmanship, quality, maintainability and performance
- Attention to detail and personal pride in your work
- Familiarity with system administration, database design, full-text search engines, machine learning
- Front-end development with HTML, CSS, AngularJS, Bootstrap
- Experience building APIs for web and mobile
- Experience with scaling for exponential user growth
- Experience developing online marketplaces
Awesome things about this position:
- As a member of a small team, you get to wear many hats. In addition to application development, we could use help with everything ranging from devops to machine-learning to map-making.
- As a core member of a small team, we expect you to understand the business and participate in top-level strategy discussions with the executive team.
- Your features get used by millions of people. The feedback loop is short. You can tell, quantitatively, if your favorite feature is well received.