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Your responsibilities will include designing end-to-end model lifecycle patterns (MLOps) to boost velocity of development for ML engineers, zero-to-one development and support of a graph ML codebase and platform, collaborating with ML engineers on performance tuning, optimizing batch data processing, and architecting pipelines to build and maintain massive graph data structures.</p>\n<p>We are looking for an experienced engineer with 8+ years of experience in ML infrastructure, including model training and model deployments. You should have hands-on experience with ML optimization, cloud-based technologies, MLOps tools, and proficiency with common programming languages and frameworks of ML. Strong focus on scalability, reliability, performance, and ease of use is essential.</p>\n<p>In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. 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As a key member of the team, you will be responsible for developing and executing a vision to improve our Ads Marketplace at Reddit. You will develop a deep understanding of our marketplace dynamics and identify areas of improvement by getting to the bottom of data, design, implement and ship algorithms to production that improve our ads marketplace efficiency.</p>\n<p>In this role, you will specialize in improving and optimizing our ads auction and pricing mechanism which will have a direct impact on upleveling the utility for both our advertiser and user values. You will also have the opportunity to work on other org-wide strategic initiatives such as supply optimization and ad relevance, where you will drive and execute on Reddit’s vision to transform Reddit into an advertising platform that shows the right ads to the right users at the right time in the right context.</p>\n<p>As a Staff Machine Learning Engineer in the Ads Marketplace Quality team, you will be an industry technical leader with domain knowledge in ads marketplace dynamics, auction and pricing, you will research, formulate, and execute on our mission to build end-to-end algorithmic solutions and deliver values to all the three-sided participants to our marketplace.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Lead and oversee the strategy development, quarterly planning and day-to-day execution of initiatives related to ads marketplace, auction and pricing.</li>\n<li>Proactively further our understanding of marketplace dynamics and develop algorithms to improve the efficiency and effectiveness of our ads marketplace, auction and pricing.</li>\n<li>Oversee end-to-end ML workflows,from data ingestion and feature engineering to model training, evaluation, and deployment,that optimizes the ads marketplace efficiency.</li>\n<li>Be a mentor, lead both junior and senior engineers in implementing technical designs and reviews. 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We aim to:</p>\n<ol>\n<li><strong>Develop upstream safety evaluations</strong> that to monitor how and when unsafe behaviors and goals emerge;</li>\n</ol>\n<ol>\n<li><strong>Create safer priors</strong> through targeted pretraining and mid-training interventions that make downstream alignment more effective and efficient</li>\n</ol>\n<ol>\n<li><strong>Design safe-by-design architectures</strong> that allow for more controllability of model capabilities</li>\n</ol>\n<p>In addition, we will conduct the foundational research necessary for understanding how behaviors emerge, generalize, and can be reliably measured throughout training.</p>\n<p><strong><strong>About the Role</strong></strong></p>\n<p>The Pretraining Safety team is pioneering how safety is built into models before they reach post-training and deployment. In this role, you will work throughout the full stack of model development with a focus on pre-training:</p>\n<ul>\n<li>Identify safety-relevant behaviors as they first emerge in base models</li>\n</ul>\n<ul>\n<li>Evaluate and reduce risk without waiting for full-scale training runs</li>\n</ul>\n<ul>\n<li>Design architectures and training setups that make safer behavior the default</li>\n</ul>\n<ul>\n<li>Strengthen models by incorporating richer, earlier safety signals</li>\n</ul>\n<p>We collaborate across OpenAI’s safety ecosystem—from Safety Systems to Training—to ensure that safety foundations are robust, scalable, and grounded in real-world risks.</p>\n<p><strong><strong>In this role, you will:</strong></strong></p>\n<ul>\n<li>Develop new techniques to predict, measure, and evaluate unsafe behavior in early-stage models</li>\n</ul>\n<ul>\n<li>Design data curation strategies that improve pretraining priors and reduce downstream risk</li>\n</ul>\n<ul>\n<li>Explore safe-by-design architectures and training configurations that improve controllability</li>\n</ul>\n<ul>\n<li>Introduce novel safety-oriented loss functions, metrics, and evals into the pretraining stack</li>\n</ul>\n<ul>\n<li>Work closely with cross-functional safety teams to unify pre- and post-training risk reduction</li>\n</ul>\n<p><strong><strong>You might thrive in this role if you:</strong></strong></p>\n<ul>\n<li>Have experience developing or scaling pretraining architectures (LLMs, diffusion models, multimodal models, etc.)</li>\n</ul>\n<ul>\n<li>Are comfortable working with training infrastructure, data pipelines, and evaluation frameworks (e.g., Python, PyTorch/JAX, Apache Beam)</li>\n</ul>\n<ul>\n<li>Enjoy hands-on research — designing, implementing, and iterating on experiments</li>\n</ul>\n<ul>\n<li>Enjoy collaborating with diverse technical and cross-functional partners (e.g., policy, legal, training)</li>\n</ul>\n<ul>\n<li>Are data-driven with strong statistical reasoning and rigor in experimental design</li>\n</ul>\n<ul>\n<li>Value building clean, scalable research workflows and streamlining processes for yourself and others</li>\n</ul>\n<p><strong><strong>About OpenAI</strong></strong></p>\n<p>OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. 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