Senior Machine Learning Engineer at RealtyShares

Senior Machine Learning Engineer

RealtyShares, San Francisco

Real estate investing made easy. Over $500 million invested across 1,000+ deals

RealtyShares is a leading online real estate investment marketplace where individual and institutional investors can participate in private deals across the country. By infusing technology into the archaic real estate industry, we’re able to connect accredited and institutional investors with pre-vetted real estate companies and operators looking to raise debt and equity capital. We’re giving people the chance to invest in both residential and commercial real estate properties and tap into the multi-trillion dollar real estate industry. To date, we’ve funded more than $500 million across hundreds of deals in 31 states, and have returned over $80 million of principal to our investors.

We are a venture backed startup with funding from 500 Startups, Union Square Ventures, General Catalyst and Menlo Ventures. We are located in San Francisco and growing rapidly.

RealtyShares is seeking an experienced and motivated Senior Machine Learning Engineer to join our dynamic team as we continue to create the leading online marketplace for real estate investing.

Expectations

Architect, implement, and maintain the company's data pipelines and predictive modeling systems
Lead an elite cross-functional data engineering team to achieve industry-first business breakthroughs in automated sourcing, underwriting, and servicing commercial real estate investments
Work with business stakeholders and other engineers to optimize model training parameters, guided by mission-critical business objectives

Required skills

B.S. in Computer Science or equivalent
Demonstrated production experience building data pipelines (SQL, noSQL, Spark, Hadoop, Pandas) and utilizing machine learning libraries (SciKit, Keras, TF, theano, NumPy)
5+ years of using Python in production
3+ years of production experience using machine learning to achieve mission-critical business objectives
Deep understanding of statistical methods, time series analysis, machine learning algorithms, and data structures
Proved experience with supervised and unsupervised machine learning algorithms for regression, classification, and clustering
A blend of data engineering, machine learning and product innovation skills
Experience with the GitHub Flow process
A strong focus on business outcomes
Exceptional verbal and written communication skills
Comfortable with collaboration, open communication and reaching across functional areas
Capable of quickly troubleshooting complex problems, understanding dependencies and deducing root causes
Direct interaction with internal users to gather requirements and address ongoing ad-hoc requests

Preferred skills

A graduate degree in Computer Science
Experience programming GPUs
Participation in Kaggle competitions

Benefits

Medical, dental, and vision coverage
401(k)
Stock options
Fully-stocked fridge with beverages and snacks
Flexible hours and vacation time

At RealtyShares, we value diversity and always treat all employees and job applicants based on merit, qualifications, competence, and talent. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.

*All applications receive a response

*All applications are kept strictly confidential

*No recruiters please

About RealtyShares

About us RealtyShares is a real estate crowdfunding platform. We've created the leading marketplace for real estate investing through which individual and institutional investors can purchase shares in vetted residential and commercial real estate properties for as little as $5,000 from the convenience of a laptop or tablet. These investments are offered through real estate borrowers/sponsors looking for access to more efficient capital than what banks and other private capital sources are able to provide.