Lead Machine Learning Engineer

Requisitos y responsabilidades

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Details

Collaborate with cross-functional partners (Product Managers, Data Scientists, Data Engineers, Software Engineers, Business teams) to build data and Machine Learning products
Take ownership of objectives and key results for your workstream, and own technical solutions in partnership with your manager
Architect and build robust systems to train, deploy, run inference, and monitor Machine Learning and AI systems at scale
Champion code quality, reusability, scalability, maintainability, and security, and provide input into strategic architecture decisions
Implement processes and tools to ensure data quality, enforce data governance policies and engineering best practices
Integrate Machine Learning and AI systems with production applications
Innovate with new approaches, staying abreast of current research and latest technologies in the broader ML engineering community

Required Experience & Skills:

Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or other quantitative field
7+ years of experience as an engineer specialized building Machine Learning systems
2+ years of technical leadership delivering machine learning solutions in partnership with engineers, scientists, and business stakeholders
Strong programming skills in Python and understanding of core computer science principles
Experience with frameworks and libraries for machine learning & AI such as scikit-learn, HuggingFace, PyTorch, Tensorflow/Keras, MLlib, etc.
Ability to design, train, and evaluate machine learning and AI models while adhering to best practices including model selection, validation, bias/variance tuning, performance assessment, sensitivity analysis, dimensionality reduction, etc.
Experience with MLOps practices such as automated model deployment, model performance monitoring, data drift detection, etc.
Experience with building batch and streaming pipelines using complex SQL, PySpark, Pandas, and similar frameworks
Experience with data warehouses (e.g., dimensional modeling), data lakes/Lakehouses, and other data architectures
Experience orchestrating complex workflows and data pipelines using Airflow or similar tools
Ability to load test deployed models at scale to identify performance bottlenecks
Experience with Git, CI/CD pipelines, Docker, Kubernetes
Experience with architecting solutions on AWS or equivalent public cloud platforms
Experience with developing data APIs, Microservices and event driven systems to integrate ML systems
Familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production
Experience in assessing and implementing new data tools to enhance the machine learning stack
Strong interpersonal and verbal communication skills
Technical leadership experience and the ability to mentor and guide others

Required Experience & Skills:

Knowledge of data mesh concepts
Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science
Knowledge of vector databases, knowledge graphs, and other approaches for organizing & storing information
Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, DataCataloging tools, Data Observability tools and Data Governance tools

Required Experience & Skills:

The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of thisposting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location,and we may ultimately pay more or less than the posted range. This range may be modified in the future.
We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees.
This job is eligible to participate in our long-term incentive programs.

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