ML Engineer, ETA Systems — Hybrid (3 days in-office)

Socotra, Inc.; San Francisco (CA); Hybrid; USD 141,000 - 176,000; Full time

Benefits offered by this job

Medical/dental/vision

Mental health benefits

Family building benefits

Parental leave

Commuter benefits

Lyft credits

Job summary

Lyft is seeking a Machine Learning Engineer to join the ETA team in San Francisco. You will build and maintain models estimating ETAs for ride requests to improve matching, pricing estimates and user experience. Our stack includes AWS, Kubernetes, Go, Spark, Python and Airflow.

Collaborate across software engineering, ML, and data science to deliver reliable, low-latency predictions at scale. Requirements include a B.S./M.S./Ph.D. in CS or a quantitative field and 3+ years in ML.

Qualifications

• B.S., M.S., or Ph.D. in Computer Science or other quantitative fields or related work experience.
• 3+ years of Machine Learning experience.
• Nice-to-have: Experience with big data processing / distributed data pipelines and tools such as Apache Airflow and Spark.
• Ability to work in distributed teams spread across time zones (North America and Europe).

Responsibilities

• Perform data analysis and build proof-of-concept to explore and compare ML and non-ML solutions.
• Be able to make effective tradeoffs between model accuracy, productization complexity and runtime performance.
• Develop statistical, machine learning, or optimization models.
• Write production quality code that can scale to millions of requests per day.
• Participate in code reviews, design reviews, production on-call support and incident triaging.
• Write well-crafted, well-tested, readable, maintainable code.

Skills

Machine Learning

Data analysis

Python

Spark

Airflow

Go

AWS

Kubernetes

Education

B.S./M.S./Ph.D. in CS or quantitative field

Tools

Apache Airflow

Apache Spark

Job description

Lyft is seeking a Machine Learning Engineer to join the ETA team in San Francisco. You will build and maintain models estimating ETAs for ride requests to improve matching, pricing estimates and user experience. Our stack includes AWS, Kubernetes, Go, Spark, Python and Airflow.

Collaborate across software engineering, ML, and data science to deliver reliable, low-latency predictions at scale. Requirements include a B.S./M.S./Ph.D. in CS or a quantitative field and 3+ years in ML.