Software Engineer Intern, Machine Learning (Summer 2027 - Toronto)
Lyft is hiring a Software Engineer Intern, Machine Learning (Summer 2027 - Toronto) (Canada). Level rates it ; you can apply on Lyft.
Level's read on this job
- On Level's scale, this software engineer role is Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include PyTorch, TensorFlow and scikit-learn.
- Level classifies it as an internship.
- Level lists 167 other open roles at Lyft.
AI in this role
Design, build, and deploy machine learning models and pipelines for Lyft's next-generation ML platform.
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 flourishing domain with a lot of data and creative solutions in Marketplace, Mapping, Fraud, Growth and beyond. We're actively building the next-generation Machine Learning (ML) platform for low-cost, ultra-immersive transportation to improve people’s lives using modern ML with peta-byte scale data. Our Machine Learning Engineers are excited to work on these challenging problems and redefine solutions to directly impact various aspects of Lyft's primary business.
If you are a student with experience in machine learning workflows, passionate about solving challenging problems using data and working in a dynamic, creative, and collaborative environment, this opportunity is for you!
Responsibilities:
- Contribute to the design, build, train and test of Machine Learning models
- Write production-level code to convert ML models into working pipelines
- Partner with Product Managers, Data Scientists, and fellow ML Engineers to frame Machine Learning problems within the business context
- Analyze experimental and observational data, communicate findings to support decisions
- Participate in code and spec reviews to ensure code quality and distribute knowledge
Experience:
- Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science or a related technical field from a university in Canada (required), with a graduation date between December 2027 and Summer 2028 (required). For any candidates who are master's students who worked between their bachelor's and master's programs: candidates should also have less than 2 years of relevant full-time work experience
- Available during Summer 2027 for the internship in Toronto
- Good understanding and knowledge of ML libraries like scikit-learn, Tensorflow, PyTorch, Keras, MXNet, etc.
- Strong programming skills in Python or a similar object oriented language
- Proven ability to effectively turn research ML papers into working code
- Practical knowledge of how to build efficient end-to-end ML workflows
- “Engineer at heart” with a high degree of comfort in designing software systems and producing high-quality code
- Curiosity and ability to quickly learn new concepts and technologies
- Strong problem solving mindset, resourcefulness, and willingness to figure things out independently through research or collaboratively through brainstorming
- Demonstrated oral and written interpersonal skills
Benefits:
- Mental health benefits
- In addition to holidays, interns receive 2 days paid time off and 3 days sick time off
- Subsidized commuter benefits and Lyft ride credits
Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.
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 at least 3 days per week, including 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. #Hybrid
The expected base pay range for this position in the Toronto area is CAD $46 - CAD $53 per hour. 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.
Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.
This is a new position.
How we rate this
Software Engineer Intern, Machine Learning (Summer 2027 - Toronto) at Lyft rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- Typical software engineer job
- $168k
This posting doesn't list pay. Use the typical software engineer figure when pay comes up. See software engineer jobs
Questions you could be asked
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where deep learning was part of your work. What did you do?
- Tell me about a project where mlops was part of your work. What did you do?
- Tell me about a project where software engineering was part of your work. What did you do?
- Walk me through how you've used PyTorch in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Machine learning, Deep learning, MLOps, Software Engineering, and PyTorch. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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