Deep Learning Research Engineer Intern

🔥 14 hours ago

🍂 Massachusetts – Remote

🍂 Massachusetts – Remote

👨‍🎓 Internship

⚪️ Entry-level

📚 Research Engineer

🚫👨‍🎓 No degree required

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👻 Ghost score 11%

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DataRobot

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2012

💼 Consulting

🏥 Healthcare

🏭 Manufacturing

Consulting • Healthcare • Manufacturing

DataRobot is a company that provides an AI platform and applications designed to integrate into core business processes. The company offers Enterprise AI Suite, AI Apps, and AI Platform services, which include Generative AI, Predictive AI, AI Governance, and AI Observability. DataRobot aims to help businesses develop, deliver, and govern AI solutions at scale, supporting industries such as energy, financial services, healthcare, manufacturing, and the public sector. With a focus on maximizing business impact and minimizing risk, DataRobot provides solutions that expedite deployment and secure numerous predictions each day.

📋 Description

• Design and improve the core probabilistic foundation-model architecture • Develop input encoders for temporal, unordered tabular, and mixed-modality data • Design attention factorizations across variables, rows, and horizons • Develop distributional output heads and decoding strategies for coherent joint samples • Run controlled architecture studies, including ablations, scaling behavior, memory, and throughput profiling • Build scalable PyTorch implementations supporting larger input/output spaces, improved throughput, and tighter memory budgets • Study how architecture, data generation, and inference constraints affect benchmark quality and real-world usefulness • Extend the synthetic-data engine with richer stochastic dynamics, constraints, dependence structures, heavy tails, and regime behavior when applicable • Turn research ideas into robust implementations and credible empirical results • Collaborate on research targeting state-of-the-art outcomes and practical deployment constraints

🎯 Requirements

• Strong PyTorch skills and hands-on experience building and training deep models • Solid understanding of Transformers, attention variants, and long-context or state-space sequence architectures, including tradeoffs between quality, memory, and latency • Experience with probabilistic modeling in neural networks, including distributional output heads, likelihood-based or proper-scoring-rule losses, mixture or flow models, or related uncertainty-aware learning setups • Strong foundation in probability and statistics, including reasoning about densities and masses, dependence between variables, and calibration • Ability to connect architectural ideas to working GPU-native implementations, controlled experiments, and diagnostics • Strong engineering habits, including readable code, tests, reproducible experiments, and disciplined evaluation • Ability to debug training instability, reason about changed results, and iterate from hypothesis to evidence • Working knowledge of stochastic processes and stochastic differential equations is a strong plus • Experience designing synthetic data generators or simulation-based training curricula is a strong plus • Depth in finance, energy, commodities, or a similarly quantitative field is a strong plus • Familiarity with tabular or mixed-modality deep learning is a strong plus • Post-doctoral-level experience or very close to it • PyTorch proficiency • Ability to work with GPU-native implementations

🏖️ Benefits

• Medical, Dental & Vision Insurance • Flexible Time Off Program • Paid Holidays • Paid Parental Leave • Global Employee Assistance Program (EAP) • Competitive pay and benefits