Software Engineer Ml Platform Manager in Canada

About the Team The Spark Platform team owns and operates DoorDash's Apache Spark ecosystem — the execution runtime, remote shuffle service, cluster scheduler, and reliability tooling that powers the company's data, analytics, and ML workloads. We run Spark across the company at significant scale and continue to expand the workloads, capabilities, and consumer base we serve. Orchestrating and operating thousands of Spark cluster deployments is a complex distributed system problem which the team invests heavily in runtime optimization, systems architecture, multi-tenant scheduling, and end-user tooling. About the Role As a Senior Software Engineer on Spark Platform, you will set the technical direction for our in-house Spark deployment and shape the architecture that will run DoorDash's data, analytics, and ML compute for the next five years and beyond. You will own the deep, cross-cutting problems that span the runtime, the shuffle service, the scheduler, and the overall service reliability — making the architectural calls that compound across the platform's lifetime. You will partner with the Engineering Manager on technical roadmap, hiring, and team shape, and act as the senior technical voice in cross-team partnerships with Data Engineering, ML Platform, and product engineering teams that depend on the platform. You must be located in San Francisco, Sunnyvale, Seattle, or New York City for this hybrid position. You will report into the Engineering Manager on our Spark Platform team. You're excited about this opportunity because you will… Set the multi-year technical direction for an in-house Spark-on-Kubernetes platform — runtime, shuffle, scheduler, reliability — and make the architectural calls that compound for years. Own the deepest distributed-systems problems on the team: shuffle architecture, multi-tenant scheduling, runtime performance, and the failure modes that only show up at scale. Partner with the Engineering Manager on technical roadmap, hiring, inte

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, et

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. The Mapping team at Lyft is tasked with building a digital representation of the physical world - a map. We collect and serve the freshest and most accurate mapping data possible, along with algorithms, models, platform services, and map-based user experiences that power Lyft’s current and future transportation offerings. Mapping represents a huge opportunity for Lyft’s business, but also a big challenge. We build and scale systems that deal with large data storage, real-time data processing, machine / deep learning pipelines, routing and ETA models, driver and passenger location tracking, and more. We built beautiful and magical user experiences on top of all those services, and compete with companies that have been in the mapping business for decades. To strengthen our efforts, we are hiring a Senior ML Engineer who will work end-to-end on creating and improving new capabilities to detect changes in the environment and reflect them in our Lyft map using a wide variety of input sources from the Lyft fleet. For this we are looking for someone who values software engineering best practices, loves the algorithmic and geospatial side of the challenge and is data-driven from start to end. Our technology stack ranges from basic machine learning models to large language models and running them at scale on millions of images. You will work with incredibly passionate and talented colleagues from machine learning, data science, and engineering on projects that delight our passengers and drivers – powered by an up to date map. 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 proble

About the Team The Spark Platform team owns and operates DoorDash's Apache Spark ecosystem — the execution runtime, remote shuffle service, cluster scheduler, and reliability tooling that powers the company's data, analytics, and ML workloads. We run Spark across the company at significant scale and continue to expand the workloads, capabilities, and consumer base we serve. Orchestrating and operating thousands of Spark cluster deployments is a complex distributed system problem which the team invests heavily in runtime optimization, systems architecture, multi-tenant scheduling, and end-user tooling. About the Role As a Software Engineer on Spark Platform, you will execute across the surfaces of our in-house Spark deployment that serves the entire company. The work spans Spark runtime upgrades and performance, multi-tenant scheduling and executor bin-packing on Kubernetes, cluster lifecycle automation, and the observability and incident automation that keep the platform sustainable. You will move between layers as the work demands — picking up the next high-leverage problem regardless of where it sits — and partner closely with the rest of the team and with platform consumers across the company. You must be located in San Francisco, Sunnyvale, Seattle, or New York City for this hybrid position. You will report into the Engineering Manager on our Spark Platform team. You're excited about this opportunity because you will… Build and operate an in-house Spark platform that runs at company-wide scale, spanning runtime, scheduler, reliability, and user-facing tooling. Drive multi-tenant scheduling, executor bin-packing, and cost-aware placement that let a small team serve dozens of consumer teams. Own pieces of cluster lifecycle automation — provisioning, upgrades, capacity changes, and node-failure handling — at a scale where these stop being manual events. Build the observability and incident automation that make the platform debuggable end-to-end and keep on-call sus

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: Everyone at Airbnb thinks about trust, but our team obsesses over it daily. At the core of trust is safety, and thus we spend a significant amount of our time and energy keeping the community safe. The Trust team is responsible for developing the technology that helps protect our community and platform from fraud while also ensuring our hosts, guests, homes, and experiences meet our high standards. We constantly work to fight against online fraud (such as monetary loss, compromised accounts, spam and scam in messages, fake inventory, etc.) as well as offline fraud (theft, property damage, personal safety, etc.). We also work on onboarding and screening of users, and think about complex topics like identity and reputation to ensure that every interaction with Airbnb helps build trust in us and our community. The Trust Frontier AI team is where new AI technology for Trust gets invented and proven. We build specialized models for mission-critical trust and safety problems, develop the AI agents and agentic capabilities that automate trust decisions, and create the benchmarks and evaluation harnesses that keep decision quality high as those agents take on more autonomy. We work on problems before the answer is known — prototyping, experimenting, and iterating with our partner teams until a solution proves itself against real business and top line metrics. You'll work side-by-side with talented product managers, data scientists, software engineers, fraud intelligence, and operations teams. Together, you'll design and build ML solutions that have direct, meaningful impact on user trust, b

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: Everyone at Airbnb thinks about trust, but our team obsesses over it daily. At the core of trust is safety, and thus we spend a significant amount of our time and energy keeping the community safe. The Trust team is responsible for developing the technology that helps protect our community and platform from fraud while also ensuring our hosts, guests, homes, and experiences meet our high standards. We constantly work to fight against online fraud (such as monetary loss, compromised accounts, spam and scam in messages, fake inventory, etc.) as well as offline fraud (theft, property damage, personal safety, etc.). We also work on onboarding and screening of users, and think about complex topics like identity and reputation to ensure that every interaction with Airbnb helps build trust in us and our community. You'll work side-by-side with talented product managers, data scientists, software engineers, fraud intelligence, and operations teams. Together, you'll design and build ML solutions that have direct, meaningful impact on user trust, business success, and the global Airbnb community. The Difference You Will Make: As a Senior Machine Learning Engineer on the Trust team, you will actively contribute code and ideas that shape the ML systems protecting millions of Airbnb users. You'll own and deliver ML projects end-to-end — from designing and training models to productionizing and operating them at scale, while collaborating closely with cross-functional partners. You'll tackle real-world challenges such as account takeover, fake accounts, payment fraud, and bot detection. Your work

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Reddit has a flexible workforce! If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence. Team Description Reddit is poised to rapidly innovate and grow like no other time in its history. We’re currently hiring across multiple teams, some of these teams include: Ads ML Serving Team The Ads ML Serving team is part of Reddit’s Ads ML Platform, which builds the infrastructure and tools that power machine learning across Ads. This team focuses on creating a highly reliable, scalable, and efficient ML serving stack. Their work includes evolving long-term serving architecture, integrating closely with the ads serving stack, optimizing CPU/GPU performance, and building model velocity tools like observability libraries and model quality gating. Attribution & Identity Team The Attribution & Identity team builds products that help advertisers understand and measure the impact of their campaigns. They focus on attribution systems, identity solutions, and advertiser experimentation tools that improve performance insights and usability. Their goal is to make Reddit’s advertising platform more effective, transparent, and data-driven. Ads Growth Team The Ads Growth team drives initiatives to expand Reddit’s advertiser base, with a focus on Small to Medium Businesses (SMBs). We build and scale the technical founda

Director of Engineering, Physical AI Role Overview The Director of Engineering will report to the General Manager of Physical AI, and will be responsible for leading a multi-disciplinary engineering organization. In this senior leadership role, you will own the execution of the Physical AI Data Engine — the platform powering the next generation of Physical AI/Embodied AI. You will collaborate closely with Operations and GTM to guide product direction and help solve the data bottleneck that stands between today's robotics research and real-world deployment. This role requires significant ownership in a fast-paced environment and you will motivate internal teams to set the pace for business growth. Travel will come into play. Key Responsibilities: Set and drive the technical vision across data collection infrastructure, teleoperation systems, ML training pipelines, model evaluation frameworks, annotation tooling, and research Lead a multidisciplinary engineering organization—spanning engineering managers, software engineers, ML engineers, and ML research scientists—while designing the organizational structure, talent strategy, and culture required to scale rapidly without compromising on quality or strategic alignment Maintain exceptional technical and operational excellence by deeply understanding team deliverables, asking incisive questions, identifying slipping standards early, and knowing precisely when to step in Drive cross-functional alignment across Engineering, Operations, and GTM on platform architecture, release processes, and shared priorities Collaborate with researchers and clients to architect and deliver scalable, production-grade data infrastructure tailored for complex robotics workloads Required Qualifications: Bachelor's degree in Engineering, Robotics, Computer Science, or a related technical field 8+ years of engineering experience in fast-paced environments, including 4+ years direct people management demonstrated history of recruiting, mentorin

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! About the role. We’re building the next generation of agentic AI infrastructure at Cohere. This team sits at the intersection of ML systems, distributed infrastructure, and developer experience, creating the platform that powers autonomous AI agents at scale. You’ll work on hard, forward-looking problems with few established patterns, including secure code execution, agent state management, model routing, identity and authentication, and resource management for long-running agent workflows. This role is a strong fit for someone who combines systems depth with ML intuition. You should be comfortable building reliable infrastructure, thinking through distributed systems tradeoffs, and understanding how emerging agentic capabilities shape platform design. What you’ll work on. Secure execution environments for agent-generated code Identity, authentication, and trust boundaries for agents Model routing and orchestration across different model types and environments Rate limiting, quotas, and resource management for agent workflows State management, memory, and filesystem abstractions for agents. In this role you will: Turn emerging M

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this team? The internal infrastructure team is responsible for building world-class infrastructure and tools used to train, evaluate and serve Cohere's foundational models. By joining our team, you will work in close collaboration with AI researchers to support their AI workload needs on the cutting edge, with a strong focus on stability, scalability, and observability. You will be responsible for building and operating superclusters across multiple clouds. Your work will directly accelerate the development of industry-leading AI models that power Cohere's platform North. Please Note: All of our infrastructure roles require participating in a 24x7 on-call rotation, where you are compensated for your on-call schedule. As a Staff Software Engineer, you will: Build and scale ML-optimized HPC infrastructure : Deploy and manage Kubernetes-based GPU/TPU superclusters across multiple clouds, ensuring high throughput and low-latency performance for AI workloads. Optimize for AI/ML training : Collaborate with cloud providers to fine-tune infrastructure for cost efficiency, reliability, and performance , leveraging technologies like R

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’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We're looking for Masters or PhD students who are passionate about solving mathematical problems with data and are excited about working in a fast-paced, innovative and collegial environment. We are hiring for a variety of Data Science interns, focusing on the following specialties: Optimization: Construct and fit statistical or optimization models that facilitate automated decision making in the app. Machine Learning: Design, build, tune, and deploy machine learning models with a special emphasis on feature engineering and deployment. Inference: Design and analyze tests in our dynamic marketplace, estimating statistical and ML models to enable better decisions, and developing and evaluating algorithmic policies in our pricing, dispatch, and incentives systems. You will report into a Science Manager. Responsibilities: Partner with Engineers, Product Managers, and other cross-functional partners to frame problems, both mathematically and within the business context Perform exploratory data analysis to gain a deeper understanding of the problem Write production modeling code; collaborate with software engineers to implement algorithms in production Design and run both simulated and live traffic experiments Analyze experimental and observational data; communicate findings including working with partner teams and presentations; facilitate launch decisions Experience: Currently pursuing a Masters or PhD degree at a university in Canada (required) in mathematical sciences ( Opera

About the Role: Tubi is seeking a highly skilled and experienced Senior QA Automation Engineer to lead quality assurance initiatives for our cutting-edge streaming and AI-driven product features. This pivotal role involves ensuring exceptional end-to-end user experiences, robust streaming playback, and the accuracy and integrity of our AI/ML features across web, mobile, and OTT platforms. We're looking for a candidate with a strong background in streaming QA and deep technical knowledge of media workflows. You'll be instrumental in collaborating with engineering, product, and data science teams to define comprehensive QA strategies that guarantee both functional excellence and data-level quality. This is a hybrid role based out of our Toronto office. You must be willing to travel to our Toronto office three days/week. What You'll Do: Design and lead test strategies for streaming workflows, playback systems, and AI-powered features. Test across platforms (web, mobile, and connected TV) to ensure functional parity and playback stability. Validate streaming performance—including ABR logic, encoding pipelines, and DRM integrations—under diverse real-world conditions. Debug with precision using tools like Charles Proxy, Chrome DevTools, ADB, and Xcode. Collaborate with data and ML teams to validate AI model updates, recommendations, and personalization accuracy. Leverage AI-assisted QA tools to enhance regression coverage, UI validation, and anomaly detection. Contribute to automation and CI/CD frameworks, driving faster, more reliable releases. Help drive a shift-left testing approach by engaging early in the software development lifecycle, partnering with product managers, engineers, and data scientists to identify quality risks, define test strategies, and ensure testability during requirements and design phases. Oversee QA deliverables for multiple concurrent releases and ensure seamless sign-off for production launches. Monitor live environments for playback or reco

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. Machine Learning is at the heart of Lyft’s products and decision-making. Machine Learning Engineers at Lyft operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges, from pricing and marketplace frameworks that ensure reliability and competitiveness, to agentic AI platforms that automate analytical workflows, to behavioral detection systems that protect the integrity of our network. We operate at the intersection of applied ML and real business impact, shipping models that directly influence revenue, rider experience, and partner trust. Lyft Business builds products that help organizations move the people who matter most—employees, customers, patients, and guests—easily and efficiently. Our offerings include Business Travel, Lyft Pass, and Concierge (for healthcare and non-healthcare rides), enabling companies to manage transportation at scale through APIs, integrations (e.g., Concur, Expensify), and dedicated tools. These platforms power high-impact B2B use cases across corporate travel, healthcare access, customer experience, and community programs. We're looking for a Machine Learning Engineer to design, build, and deploy ML systems across Lyft Business. This is a high-scope role: you won't be siloed into one problem area. Instead, you'll move across pricing algorithms, fraud and behavior detection, agentic AI systems, and emerging ML applications as the business evolves. You'll write production-quality code, own models end-to-end from prototyping through deployment, and collaborate closely with Data Scientists, Product Managers, and Software Engineers to translate complex business problems into scalable ML solutions. This role is ideal for someone who is technically versatile, energized by variety, and wants to see th

About the Role: As a Staff Software Engineer on the ML Infrastructure team, you will collaborate closely with the Machine Learning and Product teams to build world-class machine learning inference platforms. These platforms power essential services like personalized recommendations, search, and content understanding across Tubi. A core responsibility of this team is developing and maintaining low-latency ML model serving systems that support Deep Learning, LLM, and Search models. This involves building self-service infrastructure and critical components such as the inference engine, feature store, vector store, and experimentation engine. You will improve the way we deploy and operate our services and even contribute to open-source projects. This role grants the architectural freedom to explore new frameworks, lead critical cross-functional projects, and transform the capabilities of our ML and Product teams. Responsibilities: Design and build scalable, high throughput, and low latency distributed systems using Scala Build reusable components and services that serve various ML applications like Personalization, Search, Ads and Exploration Partner closely with ML engineers to understand their challenges and limitations and develop scalable solutions to address them. Proactively recommend solutions to keep our ML Inference stack state of the art. Take a data driven approach to identifying & optimizing latency, cost, and efficiency of our infra. Lead large scale cross functional refactorings if necessary Mentor other engineers on the team on system design, effective incident management, interviewing, leveraging LLMs for work, etc. Collaborate with ML, Product, and cross functional engineering teams to define the long term vision and architecture for ML Infrastructure at Tubi. Your Background: Experience designing and building scalable, distributed systems in any modern backend language (e.g., Scala, Java, Python, Go, C++); experience with Scala or JVM b

Overview We are seeking a hands-on Director of AI Software Engineering to lead and scale AI engineering efforts supporting multiple business units across Governance, Risk, and Compliance (GRC). This role sits at the intersection of product delivery, platform evolution, and applied AI—driving real-world impact across core workflows. This is not a pure management role. We are looking for a builder who leads from the front, someone who has recently written production code, shipped systems end-to-end, and can operate comfortably in ambiguity while aligning teams and stakeholders. What You’ll Do Lead AI Engineering Across GRC Own delivery of AI-powered capabilities embedded directly into business unit workflows (e.g., risk analysis, compliance automation, reporting, due diligence) Partner with product, data, and platform teams to translate business problems into scalable AI systems Stay Hands-On Contribute to architecture, code reviews, and critical path implementation Prototype and validate new approaches (LLMs, agents, retrieval systems, classification pipelines, etc.) Set engineering standards for performance, reliability, and cost efficiency Build and Scale Teams Lead and mentor a high-performing team of AI/ML and software engineers Drive hiring, coaching, and career development Establish a culture of ownership, speed, and technical excellence Drive Execution Deliver production-grade systems—not experiments Balance speed with rigor (security, privacy, compliance) Operate across multiple concurrent initiatives with clear prioritization Communicate and Influence Act as a bridge between engineering and business stakeholders Clearly articulate trade-offs, risks, and outcomes to senior leadership Align cross-functional teams around shared goals and timelines What We’re Looking For Proven Builder 10+ years in software engineering, with recent hands-on coding experience Demonstrated track record of shipping production systems at scale Experience with modern