Salary range: $120k – $150k
Equity range: 0.03% – 0.15%
What we are looking for:
Kalepa is looking for aMachine Learning Engineer with 3+ years of experience to lead the framing, development, and deployment at the scale of machine learning models. As a Machine Learning Engineer you will lead the framing, development, and deployment at scale of machine learning models to understand the risk of various classes of businesses. You will be turning vast amounts of structured and unstructured data from many sources (web data, geolocation, satellite imaging, etc.) into novel insights about behavior and risk.
Team members are given full ownership over their projects and are expected to drive the project’s direction and maintain focus. The team works in a two-week sprint, and ML Engineers will work closely with Product Management and Software Engineers.
About you:
- You must have 3+ years of experience in engineering and data science.
- You love to hustle: finding ways to get things done, destroying obstacles, and never taking no for an answer. The words “it can’t be done” don’t exist in your vocabulary.
- You have in-depth understanding of applied machine learning algorithms, especially NLP, and statistics
- You are experienced in Python and its major data science libraries, and have deployed models and algorithms in production
- You are comfortable with data science as well as with the engineering required to bring your models to production.
- You are excited about using a wide set of technologies, ultimately focused on finding the right tool for the job.
- You value open, frank, and respectful communication.
As a plus:
- You have experience with AWS
- You have hands-on experience with data analytics and data engineering.
What you’ll get:
- Competitive salary (based on experience level).
- Significant equity options package.
- Work with an ambitious, smart, global, and fun team to transform a $1T global industry.
- 20 days of PTO a year.
- Global team offsites.
- 100% covered PPO medical, 100% covered vision and dental for individuals and families.
- Healthy living/gym stipend. Mobile phone bill stipend.
- Continuing education credits.
- 401(k) plan with employer contribution (regardless of employee contribution)