Senior Machine Learning Engineer, Data Science

Quantiphi

Sr. Machine Learning Engineer developing intelligent forecasting models and quotation automation agents at Quantiphi.

Posted 7/17/2026full-timeRemote • California • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in designing and deploying machine learning models using GCP-native services, with a strong foundation in statistics and experience in AI agent frameworks. Proficient in Python and SQL for data analysis and model management, ensuring effective collaboration with data engineering teams.

Highest-signal resume keywords

Machine Learning EngineeringGCP ML Stack ProficiencyPython ProgrammingStatistical AnalysisAI Agent Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData SciencePythonSQLStatistical AnalysisTime-Series AnalysisFeature EngineeringHyperparameter TuningModel ManagementExploratory Data Analysis

Tools & Technologies

GCPVertex AIBigQueryCloud ComposerMLflowTensorFlowPyTorchLangChainLlamaIndexWeights & Biases

Certifications & Qualifications

Bachelor's DegreeMaster's Degree

Industry Keywords

Forecasting ModelsData PipelinesAI AgentsStatistical ExperimentationModel Interpretability

Tech Stack

Tools & technologies

BigQueryCloudGoogle Cloud PlatformNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Design, develop, and deploy forecasting models for product demand, pricing trends, and quotation accuracy using GCP-native services
Conduct exploratory data analysis (EDA) and feature engineering on large-scale datasets
Build AI agents for forecasting and quotation workflows using agentic frameworks
Develop and maintain production ML pipelines on Vertex AI Pipelines and Cloud Composer
Implement statistical experimentation frameworks to validate model improvements
Collaborate with data engineering teams to design feature stores and data pipelines in BigQuery
Optimize model performance through hyperparameter tuning and interpretability techniques
Integrate ML model outputs into agentic workflows
Document model architectures and present findings to stakeholders

Requirements

What you’ll need

6+ years of experience in machine learning engineering and data science
Proficiency in Python (Pandas, NumPy, scikit-learn, statsmodels) and at least one deep learning framework (TensorFlow, PyTorch, or JAX)
Hands-on experience with GCP ML stack: Vertex AI, BigQuery, Cloud Functions, Cloud Storage, and Pub/Sub
Strong foundation in statistics, probability, and time-series analysis (ARIMA, Prophet, exponential smoothing, state-space models)
Experience building or integrating with AI agent frameworks (LangChain, LlamaIndex, Vertex AI Agents, or similar agentic orchestration tools)
Proficiency in SQL for complex analytical queries on large-scale data warehouses
Experience with experiment tracking and model management tools (MLflow, Vertex AI Experiments, Weights & Biases)
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative field

Benefits

Comp & perks

Join one of the world’s fastest-growing AI-first digital engineering companies
Make a real impact at scale
Lead and collaborate with a high-energy team
Work with Fortune 500 companies