Job description
At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
We are hiring a Machine Learning Engineer to join our ETA team. Our team builds and maintains Lyft's system responsible for estimating/predicting ETAs for every ride request on our platform. ETAs play a critical role in matching decisions, pricing estimates and overall user experience. Low latency, high reliability and high accuracy are paramount for our success.
If you are a critical thinker with experience in machine learning workflows and writing reliable code, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you.
Our technology stack runs on AWS, Kubernetes, Go, Spark, Python and Apache Airflow. In this role, you will work with incredibly passionate and talented colleagues from software engineering, machine learning and data science on building rideshare experiences that delight millions of riders and drivers.
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, its productization complexity and runtime performance
• Develop statistical, machine learning, or optimization models
• Write production quality code that can scale well to serve millions of requests per day
• Participate in code reviews, design reviews, production on-call support and incident triaging process.
• Write well-crafted, well-tested, readable, maintainable code
Experience:
• 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)
Benefits:
• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership
Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.
Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid
The expected base pay range for this position in the San Francisco area is $140,800 - $176,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.