Senior Machine Learning Engineer, Recommendations

Socotra, Inc.; San Francisco (CA); Hybrid; USD 162,800 - 203,500; Full time

Benefits offered by this job

Medical, dental, and vision insurance

Mental health benefits

401(k) plan with company match

Discretionary paid time off

Monthly Lyft credits

Job summary

Lyft, based in San Francisco, is seeking a Machine Learning Engineer to develop and launch algorithms powering core services. Ideal candidates are critical thinkers with expertise in machine learning and a passion for solving problems with data.

This hybrid role requires in-office attendance three days a week. Comprehensive benefits include medical and mental health care, generous parental leave, and monthly Lyft credits.

Base pay ranges from $162,800 to $203,500, dependent on experience and qualifications.

Qualifications

• 5+ years of Machine Learning experience.
• Strong understanding of supervised learning, forecasting, recommendation systems, reinforcement learning.
• Passion for building impactful machine learning models.

Responsibilities

• Partner with teams to apply machine learning for business impact.
• Perform data analysis and build proof-of-concept for ML solutions.
• Write production quality code to launch ML models at scale.

Skills

Machine Learning

Python programming

Golang

Data analysis

Communication skills

Education

B.S., M.S., or Ph.D. in Computer Science or related fields

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.

With over half a billion rides and counting, Lyft is solving hard problems in a rapidly growing domain with a lot of data and creative solutions in Marketplace, Mapping, Fraud, Trust & Safety, Growth and beyond. While traditional approaches to optimization and problem decomposition are sufficient to disrupt transportation, building next‑generation platform for low‑cost, ultra‑immersive transportation to improve people’s lives warrants modern ML utilizing peta‑byte scale data. Our highly motivated Machine Learning Engineers work on these challenging problems and define solutions to directly impact various aspects of our core business.

If you are a critical thinker with experience in machine learning workflows, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you.

As a machine learning engineer, you will be developing and launching the algorithms that power the platform’s core services and impactful products. Compared to similarly‑sized technology companies, the set of problems that we tackle is incredibly diverse. They cut across transportation, economics, forecasting, mapping, safety, personalization, and adaptive control. We are hiring motivated experts in each of these fields. We’re looking for someone who is passionate about solving problems with data, building reliable ML systems, and is excited about working in a fast‑paced, innovative, and collegial environment.

Responsibilities:

• Partner with Engineers, Data Scientists, Product Managers, and Business Partners to apply machine learning for business and user impact
• Perform data analysis and build proof‑of‑concept to explore and propose ML solutions to both new and existing problems
• Develop statistical, machine learning, or optimization models
• Write production quality code to launch machine learning models at scale
• Evaluate machine learning systems against business goal

Experience:

• B.S., M.S., or Ph.D. in Computer Science or other quantitative fields or related work experience
• 5+ years of Machine Learning experience
• Passion for building impactful machine learning models leveraging expertise in one or multiple fields.
• Proficiency in Python, Golang, or other programming language
• Excellent communication skills and fluency in English
• Strong understanding of Machine Learning methodologies, including supervised learning, forecasting, recommendation systems, reinforcement learning, and multi‑armed bandits

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 $162,800 - $203,500, 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.