Deep Learning Research Engineer Intern

Job not on LinkedIn

🔥 1 minute ago

🏄 California, Massachusetts, +1 more states – Remote

🏄 California – Remote 🍂 Massachusetts – Remote ☕ Washington – Remote

⏰ Full Time

⚪️ Entry-level

📚 Research Engineer

🚫👨‍🎓 No degree required

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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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 core probabilistic foundation-model architecture • Build input encoders for temporal, unordered tabular, and mixed-modality data • Develop attention factorizations across variables, rows, and horizons • Create distributional output heads and decoding strategies for coherent joint samples • Run controlled architecture studies, ablations, scaling analyses, and memory/throughput profiling • Build scalable PyTorch implementations supporting larger input/output spaces, improved throughput, and tighter memory budgets • Study interactions among architecture, data generation, inference constraints, benchmark quality, and real-world usefulness • Extend synthetic-data engines with richer stochastic dynamics, constraints, dependence structures, heavy tails, and regime behavior • Turn research ideas into robust implementations and credible empirical results

🎯 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 • 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 and iterate from hypothesis to evidence • Working knowledge of stochastic processes and stochastic differential equations • Experience designing synthetic data generators or simulation-based training curricula • Depth in a domain with rich stochastic structure such as finance, energy, or commodities • Familiarity with tabular or mixed-modality deep learning • Role designed for someone operating at post-doctoral level, or very close to it

🏖️ Benefits

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