AI & Machine Learning Engineer I

Gen

AI & Machine Learning Engineer I at Gen building AI solutions for customer growth. Collaborates with teams to deploy models improving personalization and long-term customer value.

Posted 7/28/2026full-timeMountain View • California • 🇺🇸 United StatesJuniorMid-Level💰 $176,000 - $191,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in applied machine learning, data analytics, and model development, with strong skills in Python and SQL for data processing and feature engineering. Proven ability to collaborate with cross-functional teams to deploy data-driven solutions and measure business impact through experimentation and analytics.

Highest-signal resume keywords

Applied Machine LearningData AnalyticsModel DevelopmentPython ProgrammingSQL Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel EvaluationFeature EngineeringA/B TestingStatistical AnalysisHyperparameter TuningData ProcessingPredictive ModelingRecommendation SystemsCausal Inference

Soft Skills

Collaborative CommunicationOwnership MindsetGrowth Mindset

Tools & Technologies

BigQuerySparkML LibrariesCloud Data PlatformsAI Coding Assistants

Industry Keywords

Customer PersonalizationDecisioningBusiness Impact MeasurementBehavioral Data AnalysisTransactional Data Analysis

Tech Stack

Tools & technologies

BigQueryCloudPythonSparkSQL

About the role

Key responsibilities & impact

Own well-defined machine learning projects from data exploration and model development through validation, deployment, and iteration.
Build and improve predictive, recommendation, ranking, segmentation, uplift, and customer-value models for customer personalization and decisioning.
Prepare datasets, define modeling targets, develop features, and ensure data quality for training and evaluation.
Design and analyze A/B tests, holdouts, and offline evaluations to measure model performance and business impact.
Work with engineering, product, analytics, and business partners to integrate models into production and improve them based on results and feedback.
Use AI coding assistants, automation, and reusable tools to improve the speed, quality, and consistency of modeling and analytical workflows.

Requirements

What you’ll need

Degree requirements are flexible. A technical degree in Computer Science, Data Science, Statistics, Mathematics, Operations Research, Economics, Engineering, or a related field is helpful, but equivalent practical experience is equally valued.
Applied ML and model development: Two or more years of professional experience in applied machine learning, data science, ML engineering, applied statistics, or a related field, including experience building and evaluating models with real-world data.
Data analytics: Experience analyzing behavioral, transactional, product, marketing, or customer data and translating findings into practical insights or recommendations.
Experimentation: Experience defining success metrics, analyzing experiments, evaluating model performance, and interpreting business impact.
Collaborative delivery: Experience working with engineering, product, analytics, or business partners to deploy or apply data-driven solutions.
Relevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, or lifecycle decisioning is a plus.
Machine learning and modeling: Strong Python skills and practical knowledge of supervised learning, model selection, hyperparameter tuning, evaluation, and performance analysis.
Data processing and feature engineering: Strong SQL skills and experience using platforms such as BigQuery, Spark, or similar tools for data extraction, cleaning, preprocessing, exploration, and feature development.
Analytics and experimentation: Strong analytical and statistical reasoning, including A/B testing, holdout design, statistical significance, incrementally, and business-impact measurement.
Technical tools and workflows: Familiarity with common ML libraries, cloud data or ML platforms, version control, and AI-assisted development tools.
Ownership mindset: Takes responsibility for assigned work, follows through on commitments, and proactively addresses issues.
Business-impact orientation: Connects modeling and analysis to customer experience and measurable outcomes.
AI-first builder mindset: Enjoys modeling, analyzing, automating, and shipping while using AI tools to improve productivity and quality.
Growth mindset: Learns quickly, seeks feedback, and continuously develops technical and business knowledge.
Clear, collaborative communication: Communicates ideas, assumptions, results, and challenges effectively with technical and non-technical partners.

Benefits

Comp & perks

flexible working options
time off
competitive pay
benefits
well-being programs