Key skills
AirflowAWSCloudDockerKerasKubernetesMicroservicesPandasPySparkPythonPyTorchScikit-LearnSQLTensorflowAIMachine LearningMLGenerative AILarge Language ModelsTensorFlowscikit-learnMLOpsGitCI/CDLeadershipCommunication
About this role
Role Overview
- 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
Requirements
- 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
Tech Stack
- Airflow
- AWS
- Cloud
- Docker
- Keras
- Kubernetes
- Microservices
- Pandas
- PySpark
- Python
- PyTorch
- Scikit-Learn
- SQL
- Tensorflow
Benefits
- paid time off (vacation, holidays, sick)
- medical/dental/vision insurance
- 401(k) to eligible employees
- long-term incentive programs