Machine Learning Engineer

Machine Learning Engineer Palo Alto, San Francisco, San Mateo · Full Time Early-stage startup predicting user behavior via innovations in machine learning / big data / marketing personalization Job Description OVERVIEW We are an early-stage startup innovating in distributed systems and machine learning, with a mission to build a self-service AI to predict any user behavior.

Our goal is to help companies automatically predict their most important user outcomes - propensity to buy and churn.

We’re building a self-service platform to automatically connect disparate datasets across millions of users, extract machine learning features across billions of events, and produce predictive insights to optimize a company’s downstream marketing operations.

We’re led by a team from Google and Optimizely, and well funded by investors in Dropbox, Optimizely, and AppDynamics.

We’re in the earliest stages, but already have mid-market & public customers, pushing the limits of distributed systems and machine learning.

We dabble in Spark, Scala, Go, and Node every day, and think a lot about optimal matrix design, statistical feature extraction, and making machine learning as self-service and scalable as possible.

This is an opportunity to see a startup grow from the earliest of stages, and guide the direction of our engineering infrastructure.

If you’re interested in working at the frontier of data and machine learning, and building your own company from the ground up, we’d love to connect.

RESPONSIBILITIES -Architecture: In a short period of time, we're already at billions of events, millions of identities, and O(terabyte) datasets.

Our scalable pipeline aggregates, and transforms this data for learning to predict user behavior.

You'll be responsible for design and architecture as we scale our customer base by orders of magnitude, and add new capabilities.

-Integrations: We're building connections to dozens of public APIs and analytics services.

You'll be leading development of a scalable API driver platform.

There's a lot of complexity here, building for scale and reliability.

You will own individual API connections, building efficient feature matrix transformations, and contribute to the building blocks that make this a scalable engineering process.

-Machine Learning: We have a pipeline to aggregate and extract features from long form event data into a scalable feature matrix suitable for machine learning.

You’ll assist in scaling and automation of the pipeline, with optimizations for dimensionality reduction, algorithm selection, and hyper-parameter tuning.

-Product: We are a very small, tight-knit team.

You will work directly with the founders and early customers, influencing product features and direction.

Opportunities to work across all levels of the stack.

REQUIREMENTS -Strong computer science fundamentals.

The API connectors and data transform pipeline will be processing large event volumes, so deep understanding of algorithmic complexity, memory / compute tradeoffs, and parallel processing is required.

-Passion for learning, building, and moving fast.

With an ambitious plan for the next year, we highly value your ability and appetite for learning.

Experience with high volume data processing.

Scalable fault-tolerant API drivers, ETL, and/or machine learning at scale are a plus.

-Willing to dive deep and experiment.

We built our custom ML pipeline in house on open source tools (Apache Spark, scala, go). We've already taken a couple dives into Spark itself, tweaking the vector assembler and reaching inside model datastructures to attain high performance and unique insights not available to the general public.

-Experience with machine learning and/or public cloud.

Apache Spark in scala, go, and node.

We're using the AWS public cloud with a sprinkling of Google Cloud: Data Pipeline, EMR, Lambda, Firebase, S3, and ECS.

COMPENSATION & BENEFITS -Compensation and generous equity package commensurate with experience -Health + Dental insurance -Catered meals 2X a week -3 weeks vacation + unlimited sick leave + benefits -Family friendly office - we have kids, dogs, and sometimes both!

Skills Machine Learning, Node.js, Distributed Systems, Scala, Data Warehouse, ETL, EMR, Firebase, Apache Spark, Amazon Lambda, Go Kris Young Account Director BHO Tech San Jose, San Francisco CA Phone: x 823