Runpod - Senior Applied Scientist
Requirements
• PhD in Computer Science, Machine Learning, Statistics, Operations Research, or a related quantitative field, or an MS/BS with equivalent applied research experience. • 5+ years building machine learning systems that shipped to production and changed a business metric. • Strong software engineering skills in Python. You write code other engineers can own, and you are comfortable in large production codebases. • Depth in at least one of: large-scale optimization and scheduling, time-series forecasting, reinforcement learning or bandits, or statistical performance modeling. • Rigor in experimental design, causal inference, and evaluation. You know the difference between a result and an artifact. • Fluency with large, messy operational data: SQL, distributed data processing, and the judgment to tell a data problem from a model problem. • The ability to explain a result clearly in writing to an audience that will not read the appendix, and to turn it into a decision. • Preferred • Preferred • Experience with GPU infrastructure, inference serving, distributed systems, or cloud capacity and pricing problems. • Publications at top-tier venues (NeurIPS, ICML, ICLR, KDD, MLSys) or meaningful open-source contributions. • Experience working directly with infrastructure or platform teams rather than inside a central research group. • Having been the first or second scientist somewhere, with the scope-setting that implies. • What You'll Receive: • The competitive base pay for this position ranges from ($180,000 - $250,000). This salary range may be inclusive of several career levels at Runpod and will be narrowed during the interview process based on a number of factors, including the candidate's experience, qualifications, and location • Meaningful equity in a fast-growing company- everyone on the team receives stock options, so your impact drives our growth and you share in the upside. • Generous medical, dental & vision plans • Flexible PTO- take the time you need to recharge • Most roles are remote work first with an inclusive, collaborative teams utilizing slack as the main form of internal communication • $1,200 Home Office & Equipment Stipend- We set you up for success from day one with gear and support to create your ideal workspace
Responsibilities
• Identify and frame the problems where applied science changes platform economics or customer experience, and say plainly which ones are not worth modeling. • Design, build, and validate models on real fleet data: scheduling and placement, capacity and demand forecasting, autoscaling and cold-start policy, performance prediction, anomaly detection. • Take your own work to production with the owning squad. Define the metric, run the experiment or A/B, ship the model, and watch it in the wild. • Establish how we measure success before the model exists, including baselines, offline evaluation, and the online metric that decides whether it stays. • Write up what you did and what it means, including the negative results, so decisions rest on evidence rather than intuition. • Raise the bar for how the whole engineering org reasons about data. Review analyses, mentor engineers on experimental design, and push back when a conclusion outruns the evidence. • Partner with product, infrastructure, and the inference teams to turn findings into roadmap, and bring the uncomfortable findings to leadership directly.
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