Reddit is hiring a Machine Learning Engineer in Toronto!
Reddit is hiring Machine Learning Engineers for its Toronto location to work on large-scale applied ML across the company's Consumer and Ads organisations. The role centres on building production systems end-to-end — from research and modelling through deployment and monitoring — supporting recommendations, search and ranking, advertising, content and user understanding, and LLM-driven experiences. Engineers are placed across teams such as Feed Relevance, Search and Answers Relevance, Notifications Relevance, Ads Ranking, Ads Targeting and Retrieval, Ads Measurement Modelling, and Advertiser Optimisation, so the exact problem area depends on team fit. The posting emphasises internet-scale impact, cross-functional collaboration, and ownership of the full ML lifecycle, including pipelines, serving infrastructure, and real-time decisioning. It is a hands-on engineering position for someone comfortable moving between classical ML, deep learning, and applied AI work while contributing to technical strategy and architecture.
Key responsibilities
- Design, build, and deploy production machine learning models and systems at scale
- Own the full ML lifecycle: problem definition, feature engineering, training, evaluation, deployment, and monitoring
- Build reliable, observable data and model pipelines with automated retraining
- Apply large-scale datasets to ranking, recommendations, search relevance, prediction, content/user understanding, and optimisation
- Partner with Product, Data Science, Infrastructure, and Engineering to turn ambiguous problems into ML solutions
- Improve latency, throughput, and model quality metrics
- Research and apply state-of-the-art techniques including deep learning, graph and transformer methods, and LLM evaluation/alignment
- Contribute to technical strategy, architecture, and the long-term ML roadmap
Key requirements
- 3-5+ years building, deploying, and operating production ML systems
- Strong programming in Python, Java, Go, or similar, with solid software engineering fundamentals
- Grasp of algorithms spanning statistical learning (XGBoost, Random Forests, regressions) and deep learning architectures (Transformers, CNNs, GNNs)
- Hands-on experience with frameworks such as PyTorch or TensorFlow
- Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure
- Ability to translate ambiguous product or business problems into technical solutions cross-functionally
- Track record of improving measurable metrics through applied ML
- Preferred: recommender, search/ranking, advertising/auction, representation learning, or multimodal embedding experience
- Preferred: distributed systems and large-scale data tooling such as Spark, Kafka, Ray, Airflow, BigQuery, or Redis
- Preferred: real-time, low-latency systems, feature engineering, model optimisation, and production monitoring
- Preferred: LLM/GenAI work including evaluation, alignment, fine-tuning, distillation, RAG or agentic systems
- Preferred: advanced degree in Computer Science, Machine Learning, or a related quantitative field
- Seniority: Mid-level to Senior (3-5+ years)
- Work mode: Not specified
- Key skills: Python, Java, Go, PyTorch, TensorFlow, XGBoost, Transformers, CNNs, GNNs, Recommender systems, Search and ranking, Advertising systems, Spark, Kafka, Ray
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