Senior Machine Learning Engineer, Rider Applied AI

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 a billion rides per year and counting, Lyft is solving hard problems in a rapidly growing domain with a lot of data and creative solutions in Rider, Marketplace, Growth, and beyond. While traditional approaches to optimization and problem decomposition are sufficient to disrupt transportation, building a 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 and LLMs, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you.

We are seeking a Senior Machine Learning Engineer to join the Rider Applied AI team and lead the design, development, and deployment of state‑of‑the‑art machine learning and artificial intelligence systems. This role requires a strategic thinker who can balance high‑level system architecture with hands‑on technical implementation. You will collaborate across teams to shape the future of ride‑sharing by leveraging AI, Machine learning and Data science.

• Model Development: Design, build, train, and deploy machine learning models for real‑time applications.
• System Design: Architect scalable, reliable, and maintainable machine learning pipelines, integrating seamlessly with existing backend systems.
• Collaboration: Work closely with machine learning engineers, product managers, data scientists, and software engineers to align machine learning initiatives with business goals.
• Innovation: Stay ahead of the curve by exploring new algorithms, technologies (such as LLMs and LLM based applications), and frameworks to solve complex problems and introduce use cases for the team. Critically evaluate problems across business areas.
• Data‑Driven Decision Making: Utilize data‑driven insights to inform and refine machine learning strategies and solutions.
• Mentorship: Provide technical leadership, mentor engineers, and foster a culture of learning and collaboration.
• Code Quality: Write production‑level code to convert your ML models into working pipelines and participate in code reviews to ensure code quality and distribute knowledge.

• B.S., M.S. or Ph.D. in Computer Science or related technical field or relevant work experience.
• 5+ years (or Ph.D. with 3+ years) of experience in machine learning, data science, or related fields.
• Deep understanding of supervised/unsupervised learning, reinforcement learning, and advanced optimization techniques.
• Deep knowledge of ML libraries like scikit‑learn, Tensorflow, PyTorch, Keras, etc.
• Experience with distributed computing frameworks like Spark, Hadoop.
• Strong knowledge of cloud platforms (e.g., AWS, GCP) and containerization tools (e.g., Docker, Kubernetes).
• Proven ability to quickly and effectively turn research ML papers into working code.
• Practical knowledge of how to build efficient end‑to‑end ML workflows.
• Proven ability to tackle ambiguous problems and deliver solutions at scale.
• Strong communication and interpersonal skills for effective cross‑functional collaboration.
• "Engineer at heart" with a high degree of comfort in designing software systems and producing high‑quality code.

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• Access to a Lyft funded Health Care Savings Account
• RRSP plan with company match to help save for your future
• In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year…