Machine Learning Engineer

New

Reddit, Inc.Social media advertising

Remote - Ontario, CanadaFull-TimeMiddle

Salary not disclosed

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Job Details

Experience: 3-5+ years of experience building, deploying, and operating machine learning systems in production
Required Skills: PythonJavaMachine LearningPyTorchGoTensorflowDistributed Systems

Have 3-5+ years of experience building, deploying, and operating machine learning systems in production.
Bring strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals.
Understand machine learning algorithms, including XGBoost, Random Forests, regression, Transformers, CNNs, and GNNs.
Have hands-on experience with modern ML frameworks such as PyTorch or TensorFlow.
Have experience designing scalable ML pipelines, data processing systems, and model serving infrastructure.
Be able to translate ambiguous product or business problems into technical solutions and work cross-functionally.
Have experience improving measurable metrics through applied machine learning.
Experience with recommender systems, search or ranking, advertising or auction systems, representation learning, or multimodal embeddings is preferred.
Familiarity with distributed systems and large-scale data processing tools such as Spark, Kafka, Ray, Airflow, BigQuery, or Redis is preferred.
Experience with real-time, low-latency production environments, feature engineering, model optimization, or production monitoring is preferred.
Experience with LLM or generative AI techniques, including evaluation, alignment, fine-tuning, knowledge distillation, RAG, agentic systems, or productionizing LLM-powered products, is preferred.
An advanced degree in Computer Science, Machine Learning, or a related quantitative field is preferred.

Design, build, and deploy production-grade machine learning models and systems at scale.
Own the ML lifecycle, from problem definition and feature engineering through training, evaluation, deployment, and monitoring.
Build scalable data and model pipelines with reliability, observability, and automated retraining.
Use large-scale datasets to improve ranking, recommendations, search relevance, prediction, content and user understanding, and optimization systems.
Partner with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions.
Improve latency, throughput, and model quality metrics.
Research and apply machine learning and AI techniques, including deep learning, graph models, transformers, and LLM evaluation and alignment.
Contribute to technical strategy, architecture, and the long-term ML roadmap.

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