Entry-level Machine Learning Engineer resume example

• With a senior colleague reviewing the change, built a Python machine learning pipeline using SQL-derived viewing features, keeping training and evaluation windows separate; compared the model against a simple baseline before recommending deployment.
• Within an assigned workstream, added monitoring for prediction freshness, feature drift, and serving errors; tested a fallback ranking path so delayed features would not leave the recommendation surface empty.
• Within the assigned scope, versioned training data and model artifacts together, compared offline evaluation with serving latency, and inspected misclassified examples before approving the deployment candidate.
• Within the assigned scope, owned a PyTorch evaluation slice using frozen data and random seeds, resolving a misleading model gain caused by duplicate examples across train and test sets.
• Within the assigned scope, built a Python deployment check with Kubernetes resource limits, reducing inference failures from 14 to 3 per 1,000 replayed requests at the same load.

• The team implemented shadow traffic and automatic rollback for two model services; my scope was data preparation, quality checks, and follow-up documentation under senior review. The team completed the rollout against the approved acceptance criteria; documented my assigned work separately from the team result and confirmed it with a senior colleague.
• Within the assigned scope, investigated a discrepancy between a published metric and its source records, traced the transformation that changed the population, and corrected the calculation with a reproducible query.
• Within the assigned scope, compared the last successful data refresh with a failed run, separated missing source data from transformation errors, and reran only the affected interval. Kept the original query and corrected result together for review.

• Within the assigned scope, the team built an offline-to-online evaluation suite with slice-level metrics for 14 cohorts; my scope was data preparation, quality checks, and follow-up documentation under senior review
• 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: Algorithms, operating systems, databases, computer networks

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