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Prenuvo is hiring a Senior Backend Engineer II (Data) to build its Medical Data Server and the data foundation behind its clinical products — leading data architecture, governance, and pipelines in Python, ensuring FHIR/HL7 compliance, and mentoring engineers. Based in Portland, on-site.
Senior backend engineer at Prenuvo building the Medical Data Server: Python services and REST APIs, clinical data models (FHIR/HL7), and batch/event-driven pipelines that keep clinical data reliable for reporting, analytics, and AI workflows. Core stack is Python (FastAPI/Django/Flask) with healthcare interoperability standards.
A senior solutions-architect role at Lyft bridging the People (HR) organization and Corporate Engineering/IT: the hire translates HR business needs into technical requirements for systems like Workday and Greenhouse, and drives AI-first process redesign and automation across HR workflows. Hybrid role based in Toronto (3 days/week in office).
Senior ML engineer at Chorus, a marketing intelligence platform for nonprofits, owning the end-to-end machine learning product that trains, deploys, serves, and monitors models building real-time audience segments. Stack includes Python/FastAPI, MongoDB, AWS, Pinecone, and DuckDB/Dagster; on-site in Toronto ~4 days/week.
At Lyft, this ML engineer on the Safety and Customer Care team fine-tunes and aligns open-source LLMs (SFT, LoRA, RLHF/RLAIF/RLVR) and builds AI support and safety-case agents using LangGraph, owning evaluation and production deployment. Hybrid role based in Toronto, at least 3 days per week in office.
A Summer 2027 machine learning internship at Lyft in Toronto: the intern helps design, build, train, and test ML models, converts them into production pipelines, and analyzes large-scale experimental data. Core stack includes Python and ML libraries like PyTorch, TensorFlow, and scikit-learn, on a hybrid in-office schedule.
Senior Data Engineer on Lyft's Mapping team (hybrid in Toronto), owning data pipeline architecture that powers routing and travel estimations. Day-to-day involves technical design leadership, evolving data models, building AI tools for ETL/schema optimization, and code reviews using Kafka, Kubernetes, SQL, and cloud infrastructure.
Ledgebrook is an InsurTech MGA modernizing specialty insurance with cutting-edge technology. We seek a talented Full Stack Software Engineer to own multiple stack areas and drive features from concept to production.…
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. Lyft Ads is one of the fastest-growing commerce…
Lyft in Toronto is hiring a Backend Engineer for Trust & Safety to ship high‑impact safety features across the platform, collaborating with product, design and policy teams. You will work at the intersection of…
Develops AI-driven technical interview and assessment tools for CoderPad, designing fullstack features, production infrastructure, and internal tooling while leading architecture and code reviews.
Senior Fullstack Engineer at CoderPad building and shipping features across backend services, frontend applications, and AI-powered interviewing/assessment systems, working with Ruby on Rails, Java, Node/TypeScript, React, and multiple LLM providers.
This Senior Software Engineer role focuses on building and maintaining scalable data pipelines, offline experimentation tooling, and route simulation services for Lyft's mapping team. The position involves working with technologies like AWS, Databricks, Kubernetes, and Airflow to improve routing accuracy and travel time estimations.
Design and implement data-protection systems to make privacy the default, including redaction pipelines and privacy controls across services.
Builds backend infrastructure and APIs to connect Lyft with AI ecosystems and multimodal interfaces, enabling third-party agents to interact with Lyft services.
Build and scale data pipelines and models for Lyft’s Safety and Customer Care team, enabling real-time insights and AI agent performance tracking using Spark, Python, and SQL on AWS.
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