Machine Learning Engineer in Canada

About the Role: The Machine Learning team at Tubi drives the innovation behind personalized user experiences. With the largest inventory in the industry and hundreds of millions of viewers, we tackle problems in the space of recommendations, search, content understanding, and ads optimization that shape the future of streaming. We are seeking a Director of Machine Learning Engineering and Infrastructure to lead a hybrid team bridging advanced ML engineering with world-class infrastructure design. In this role, you will own the strategic direction and execution for scaling our machine learning capabilities while ensuring our distributed systems and infrastructure can support innovation at massive scale. You will combine technical depth with leadership excellence to guide teams that deliver both foundational ML systems and high-performance distributed services. This is a hybrid role for our Toronto office. What You'll Do: Lead and manage high-performing teams across ML engineering and ML infrastructure, fostering a culture of innovation, collaboration, and growth. Define and execute the strategic roadmap for ML systems, including recommendation, personalization, and ads optimization. Oversee the design, development, and deployment of scalable ML pipelines: data ingestion, feature engineering, model training, evaluation, and serving. Architect distributed systems to support ML workloads at scale, ensuring reliability, observability, and operational excellence. Partner closely with Product, Engineering, and Content teams to align on business goals and deliver impactful ML-driven experiences. Support best practices in experimentation, evaluation, and ML system monitoring. Ensure cost efficiency, scalability, and performance in ML infrastructure investments. Your Background: 10+ years of industry experience spanning machine learning engineering and distributed systems. 3+ years of leadership and management experience, with a proven ability to build and lead strong t

About the Team The Merchant (Mx) AI/ML team is a cornerstone of DoorDash’s merchant organization. We empower our restaurant partners to thrive on DoorDash by building intelligent, scalable AI systems that simplify operations, elevate their digital presence, and enhance customer engagement. Our world-class machine learning engineers develop production-grade AI solutions that power the entire merchant lifecycle — from onboarding and store setup to menu management, growth, and real-time order operations. The team is deeply customer-obsessed and impact-driven, focused on turning cutting-edge research in LLMs, multimodal learning, generative AI, and agentic automation into products that make our merchants more successful every day. About the Role We’re looking for an Engineering Manager to lead the Mx AI/ML team, driving the design and deployment of next-generation AI product solutions that power our merchant experiences. This is a highly cross-functional leadership role — you’ll collaborate with product, design, operations, and data science teams to define the AI roadmap, guide technical direction, and deliver end-to-end AI/ML solutions at massive scale. You’ll lead a team of talented ML engineers who are building real AI products delivered to merchants’ fingertips, helping them become more successful on DoorDash. You’re excited about this opportunity because you will… Lead and grow a team of exceptional AI/ML engineers developing production-grade machine learning and generative AI solutions for the merchant ecosystem. Define a multi-year strategy and execute the AI roadmap across key Mx pillars — Onboarding, Menu Media Understanding & Generation, Menu Metadata Intelligence, and Agentic Task Automation. Partner with product and operations to translate merchant pain points into scalable AI-powered solutions. Own the end-to-end lifecycle of ML systems — from ideation and experimentation to productionization and continuous improvement. Drive innovation in multimodal 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. We are hiring a Machine Learning Engineer to join our ETA team. Our team builds and maintains Lyft's system responsible for estimating/predicting ETAs for every ride request on our platform. ETAs play a critical role in matching decisions, pricing estimates and overall user experience. Low latency, high reliability and high accuracy are paramount for our success. If you are a critical thinker with experience in machine learning workflows and writing reliable code, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you. Our technology stack runs on AWS, Kubernetes, Go, Spark, Python and Apache Airflow. In this role, you will work with incredibly passionate and talented colleagues from software engineering, machine learning and data science on building rideshare experiences that delight millions of riders and drivers. Responsibilities: Perform data analysis and build proof-of-concept to explore and compare ML and non-ML solutions Be able to make effective tradeoffs between model accuracy, its productization complexity and runtime performance Develop statistical, machine learning, or optimization models Write production quality code that can scale well to serve millions of requests per day Participate in code reviews, design reviews, production on-call support and incident triaging process. Write well-crafted, well-tested, readable, maintainable code Experience: B.S., M.S., or Ph.D. in Computer Science or other quantitative fields or related work experience 3+ years of Machine Learning experience Nice-to-have: Experience with big data processing / distributed data pipelines and tools such as Apache Airflow and Spark Ability to work in distributed teams spread across time zones. (North America and

At Vanta, our mission is to help businesses earn and prove trust. We believe that security should be monitored and verified continuously, and we empower companies to practice better security and prove it with ease. Vanta has a kind and talented team, and while some have prior security experience, many have been successful at Vanta without it. As a Senior Applied AI Engineer at Vanta, you will play a crucial role in shaping Vanta’s AI offerings, setting technical strategy, and leading projects that leverage AI to deliver smarter, faster outcomes for our customers. You'll be part of a team integrating AI into the Vanta product, working alongside a multidisciplinary group of product engineers, machine learning engineers, product managers, designers, and security and compliance experts to implement, scale, and maintain AI-enabled product experiences. In this role, you’ll build products that enable Vanta’s customers to leverage AI to accelerate their journey towards compliance, managing risk, and earning trust. Visit our Vanta Engineering Blog to learn more about what our team is working on! What you’ll do as an engineer working on Applied AI at Vanta: Work cross-functionally to design and implement AI-powered features to deliver customer value and integrate LLMs with Vanta’s existing products and systems. You’ll work with other product engineers across Vanta to understand how AI systems can accelerate product adoption at Vanta Instrument evaluations, guardrails, and monitoring, and review customer usage to continually improve quality Collaborate with AI Platform engineers shaping foundational AI systems and tooling that accelerate product teams Make pragmatic tradeoffs that consider business priorities, user experience, and a sustainable technical foundation Mentor engineers, champion good technical and product instincts, and model a collaborative, high-ownership engineering culture How to be successful in this role: At least 7 years of industry experience as a software

Join us in building the future of finance. Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading. About the team + role We are building an elite team of bold thinkers and sharp problem-solvers who are wired to make an impact. The Ops Platform organization develops internal platforms that replace repetitive manual processes with AI-driven systems. These tools support key areas such as Fraud Operations, Account Operations, Financial Crimes Operations, and Retirement Services. The team works closely with product, data science, and operations partners to deliver reliable systems that improve decision-making and efficiency! As a Software Developer, you will design and build platforms that enable operational teams to investigate and resolve issues more quickly and accurately. You will work with large datasets and signals to create tooling that supports fraud investigation and other operational workflows. You will collaborate with data scientists and machine learning engineers to translate manual processes into automated systems. Your work will focus on improving system reliability, reducing operational effort, and increasing the speed at which new products and features can be supported across Robinhood’s offerings. This role is based in our Toronto, ON office, with in-person attendance expected at least 3 days per week. At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams. What you’ll do You will define technical direction and make architectural decisions for systems that support operational workflows across multiple product lines You will build tools that proces

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 role? As a Senior Machine Learning Engineer specializing in synthetic data, you will play a pivotal role in developing the synthetic data pipeline that is crucial to Cohere’s advanced language models. Your responsibilities will encompass the end-to-end management of synthetic data, including maintaining and optimizing the synthetic data pipeline, data analysis and generation, as well as conducting data ablations and model evaluation to gauge data quality. You will work with diverse web data and code data and transform them using generative models to improve token efficiency and model quality. By combining research and engineering, you will bridge the gap between raw data and cutting-edge AI models, directly contributing to improvements in critical training metrics like throughput and accelerator utilization. Your work will be essential to Cohere’s mission of delivering efficient and reliable language understanding and generation capabilities, driving innovation in natural language processing. If you are passionate about transforming data into the foundation of AI systems, this role offers a unique opportunity to make a

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 role? As a Machine Learning Engineer specializing in pretraining data, you will play a pivotal role in developing the data pipeline that underpins Cohere’s advanced language models. In this role, you will conduct data ablations to evaluate data quality and construct pre-training data mixtures to enhance model performance. By combining research and engineering, you will bridge the gap between raw data and cutting-edge AI models, directly contributing to improvements in critical training metrics like throughput and accelerator utilization. Your work will be essential to Cohere’s mission of delivering efficient and reliable language understanding and generation capabilities, driving innovation in natural language processing. If you are passionate about transforming data into the foundation of AI systems, this role offers a unique opportunity to make a meaningful impact. Please Note: We have offices in London, Paris, Toronto, San Francisco and New York but also embrace being remote-friendly! There are no restrictions on where you can be located for this role between EST and EU. As a Member of Technical Staff, Pre-Training D

About Scale Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust. About the Team Applied Intelligence Systems (AIS) is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. AIS spans multiple workstreams — agent evaluation and oversight, orchestration and tool-use infrastructure, model and systems optimization, and applied research on new agent capabilities — and this role is not scoped to any single one of them. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like. About the Role As a Staff Machine Learning Research Engineer, you will operate across the full breadth of AIS’s technical needs — wherever the hardest ML problem in agentic AI happens to be that quarter. This could mean training and fine-tuning models, designing evaluation and observability systems, building improvement loops from production data, prototyping novel agent architectures, or designing internal systems and tooling that boost productivity across teams. You’re not tied to one team’s roadmap; you’re expected to move to where the technical leverage is highest, and t

About the Team DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is our evaluation platform — the unified evals backbone that lets teams measure, trace, and trust the quality of LLM and agent systems across the company, powering trace/score ingestion, LLM-as-judge workflows, agent simulations, and LLM observability for the tens of millions of daily requests flowing through our LLM Gateway. We also own core platform surfaces including the Agent Gateway, open-weights model serving and batch inference, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, with a primary focus on our evals and LLM observability platform: the systems that let teams evaluate, trace, and continuously improve the quality of LLM and agent products. You’ll work across evaluation frameworks and SDKs, OpenTelemetry-based trace/score ingestion, LLM-as-judge and offline/online eval pipelines, agent simulations, data pipelines, backend services, and observability. This role is ideal for an engineer who enjoys building reliable measurement and quality primitives in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and evaluation methodologies are evolving quickly. You’re excited about this opportunity because you will… Build the infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Work on our unified evals platform — evaluation SDKs, OpenTelemetry trace/score ingestion, LLM-as-judge, offline and online eval pipelines, and agent simulations — alongside the LLM Gatew

About the Team DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, leading the design and architecture of our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll set technical direction across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ambiguous, high-impact systems and pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly. You’re excited about this opportunity because you will… Lead the design of infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Own and evolve our open-weights serving stack — real-time GPU endpoints, high-thr

Senior Machine Learning Developer Location: Montreal, Quebec, Toronto, or Ontario About Numa Numa is building the platform to power AI-native dealerships, rearchitecting automotive service and sales with advanced AI agents that automate customer interactions, streamline operations, and reimagine how dealerships work. Numa integrates AI into every aspect of dealership functions—from rescuing customer calls and voicemails that generate more revenue, to reducing customer resolution times that drive overall customer satisfaction (CSI), to improving dealership team productivity and accountability. Numa has raised $50 million from leading investors (Google, Threshold, Costanoa, Mitsui, and Touring Capital). The Role We’re hiring a Senior Machine Learning Developer to build and ship ML/AI systems that interact with real customers thousands of times a day. Our voice agents book service appointments, rescue missed calls, and route callers through natural conversations. You’ll work across products and platforms. You’ll ship AI features including prompts, agents, tools, and production ML models, while building the evaluations and tooling that help teams ship with confidence. You’ll also contribute to our ML platform, including model serving, LLM infrastructure, and production observability. At Numa, we believe great ML is about more than building bigger models. It’s about knowing whether a change is good enough to ship. Our evaluation-first approach makes that measurable in CI and production. What You’ll Do Build conversational AI systems for phone and SMS that understand customer needs, take action, and know when to act autonomously Develop tooling such as memory, knowledge graphs, and validated customization that help agents reason and adapt to dealership needs Train, evaluate, and deploy ML models using Ray Serve and Dagster for prediction, classification, ranking, and capacity forecasting, and keep them healthy in production Create offline and online evalua

Scale works with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling). This role will focus on optimizing data curation and eval to enhance LLM capabilities in both text and multimodal modalities. In this role, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models. You will: Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities. Design and experiment new approaches to preference optimization. Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness. Publish research findings in top-tier AI conferences. Ideally you’d have: Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field. Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning. Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning. Excellent written and verbal communication skills Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals Previous experience in a customer facing role. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined du

Scale works with the industry's leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling) and evaluation. This role is on the evaluation pod within the GenAI Research Organization and will focus on building benchmarks and diagnosing model failure modes in both text and multimodal modalities. In this role, you will develop rigorous evaluations and diagnostic methods that reveal where frontier models fail and why. You will collaborate with researchers and engineers to define best practices in evaluation-driven AI development. You will also partner with top foundation model labs to translate failure analysis into technical and strategic input on the next generation of generative AI models. You will: Analyze model behavior to identify, characterize, and diagnose failure modes in frontier LLMs and Agents. You’ll identify everything from capability gaps and reasoning errors to robustness and alignment issues, all focusing on RCA. Design and build benchmarks and evaluation methods that measure LLM capabilities in both text and multimodal modalities. Apply post-training expertise (SFT, RLHF, reward modeling) to connect observed failures to the data and training interventions that address them. Publish research findings in top-tier AI conferences. Ideally you’d have: Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field. Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning. Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning, and with LLM evaluation or benchmark development. Excellent written and verbal communication skills. Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals. Previous experience in a customer facing r

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 role? Ship state of the art models to production. Design and implement novel research ideas. Build elegant training/deployment pipelines. Join us at a pivotal moment, shape what we build and wear multiple hats as an intern! Our recruitment process will begin in the upcoming weeks, and we will be carefully reviewing applications and assessing potential candidates for our internships. Should we find a suitable match with your qualifications and our requirements, we will be in touch to discuss the opportunity further and to advance your application to the next stage Please Note: To be eligible for this position you should be a student currently enrolled in a post-secondary program, available for a full-time 3-6 month internship, co-op, or research work term. As a Machine Learning Intern, you will: Design, train and improve upon cutting-edge models. Help us develop new techniques to train and serve models safer, better, and faster. Train extremely large-scale models on massive datasets. Explore continual and active learning strategies for streaming data. Learn from experienced senior machine learning technical staff. Work c

About Scale At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations. About the ACE team The Agent Capabilities & Environments (ACE) team, part of Scale’s Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes research on agent environments and RL reward signals, benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more. About This Role This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents, such as browser and SWE agents. The ideal candidate will explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation. This position requires not only expertise in LLM agents and planning algorithms but also creativity in addressing novel challenges related to data, interaction, and evaluation. You will contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate t