Internship Research Scientist resume example

• Under supervision, reproduced a baseline using pinned dependencies and held-out tasks, reported confidence intervals across seeds, and included failed ablations rather than only the strongest run.
• The team developed a machine learning research agenda for reliable multi-step reasoning, comparing verifier-guided training against a fixed supervised baseline on held-out tasks; I supported research preparation, validation, and documentation under mentor review. The team separated genuine generalization from benchmark contamination and retained only repeatable gains in the experiment report; confirmed with my mentor that my contribution was limited to the assigned support and validation work.
• The team built high-performance implementations of deep learning algorithms in PyTorch, profiling activation memory and throughput before changing batching and checkpointing; I supported research preparation, validation, and documentation under mentor review. The team reduced a fixed evaluation run from 11.4 to 8.1 GPU-hours while preserving the same prompts, seeds, and quality checks; confirmed with my mentor that my contribution was limited to the assigned support and validation work.
• Under supervision, owned a machine-learning ablation using fixed seeds and peer-reviewed assumptions, resolved a confounded baseline and completed a reproducible report of negative results.
• Under supervision, built a high-performance implementation using profiled kernels, reduced evaluation runtime by 28% at equal accuracy and published limitations relevant to the research agenda.

• In a mentor-reviewed simulation, developed a machine learning research agenda for reliable multi-step reasoning, comparing verifier-guided training against a fixed supervised baseline on held-out tasks; separated genuine generalization from benchmark contamination and retained only repeatable gains in the experiment report.
• As a second supervised exercise, built high-performance implementations of deep learning algorithms in PyTorch, profiling activation memory and throughput before changing batching and checkpointing; reduced a fixed evaluation run from 11.4 to 8.1 GPU-hours while preserving the same prompts, seeds, and quality checks.
• In a mentor-reviewed research simulation, drafted a research agenda for a twelve-week machine learning project and explained how choosing impactful research problems differed from chasing a leaderboard; maintained weekly logs for the long-running project, with the mentor reviewing each change of hypothesis.
• In the same mentor-reviewed simulation, developed a machine learning technique for verifier-guided reasoning with a fixed token budget, comparing uniform sampling, uncertainty-based allocation, and a supervised baseline; contamination checks and component ablations isolated the allocation effect from the benefit of simply spending more compute.
• In the same mentor-reviewed simulation, built high-performance implementations of deep learning algorithms in PyTorch, profiling activation memory and throughput before changing batching and checkpointing; reduced a fixed evaluation run from 11.4 to 8.1 GPU-hours while preserving the same prompts, seeds, and quality checks.
• In the same mentor-reviewed simulation, collaborated with peers in evaluation and infrastructure to test generalizable ideas at large scale; ran the same baseline across three model sizes and rejected an apparent gain that disappeared on the held-out domain, preventing an unsupported full-scale run.
• In the same mentor-reviewed simulation, wrote the first-author project report for a verifier-budget study, testing new ideas for allocating extra reasoning steps to uncertain answers; ablations showed that selecting harder examples mattered more than simply generating longer answers, and independent reviewers reproduced the comparison from the run manifest.
• In the same mentor-reviewed simulation, assessed impacts of AI technology by testing confident errors and abstention in unfamiliar domains; reviewers approved a failure-case appendix and release limitation after benchmark accuracy failed to reduce unsupported answers reliably.
• Generated synthetic source records with duplicates, late arrivals and corrected values; wrote assertions for row counts and key uniqueness, and recorded the expected effect of each case on the reported metric.
• Compared the analytical output with a manually calculated reference table, traced differences to a transformation step, and retained a data dictionary and rerun instructions alongside the corrected query.
• Owned the synthetic-data validation using a manually calculated reference, resolved duplicate-key inflation and completed a notebook that reproduces the corrected totals.

Relevant coursework: Machine learning, optimization, statistical inference, reproducible experiments; dissertation on model evaluation

• Published an independent methods note using reproducible queries, explaining the data grain, excluded records and sensitivity of the result to a changed denominator.