14 hours ago
Remote, United StatesMid Level
H1B sponsor
Base Salary
$110k - $209k/yr
Responsibilities
- Own small to medium components of machine learning systems from technical design through implementation and delivery.
- Translate technical requirements into maintainable code and deliver workstreams according to plan.
- Build and maintain data pipelines and feature engineering workflows for machine learning and AI solutions.
- Design, train, evaluate, and refine machine learning models using sound statistical and engineering practices.
- Deploy ML solutions as microservices, APIs, batch jobs, or streaming components.
- Implement monitoring metrics for model performance, data drift, anomalies, and retraining triggers.
- Collaborate with Data Engineers, Software Engineers, Data Scientists, Product partners, and business stakeholders.
- Contribute to system design, data model, implementation, and technical tradeoff decisions.
- Follow governance, documentation, coding, and source control standards.
- Document and communicate technical decisions, progress, and outcomes to technical and non-technical audiences.
Requirements
- Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or another quantitative field.
- At least 3 years of practical experience building, evaluating, scaling, and deploying machine learning pipelines with Python.
- Strong Python programming skills and understanding of core computer science principles.
- Experience with Pandas, PySpark, and machine learning libraries such as scikit-learn, HuggingFace, TensorFlow/Keras, PyTorch, or MLlib.
- Experience with MLOps practices including automated model deployment, model performance monitoring, and data drift detection.
- Working knowledge of SQL and relational data structures.
- Ability to design, train, and evaluate machine learning models using model selection, validation, bias/variance tradeoffs, and performance assessment.
- Familiarity with batch and streaming data pipeline concepts including ETL, ELT, and stream processing.
- Experience with cloud environments, preferably AWS.
- Familiarity with APIs, microservices, Docker, and Kubernetes.
- Preferred knowledge of recommender systems, fraud detection, personalization, or marketing science.
- Preferred experience managing and architecting solutions on AWS.
- Preferred familiarity with large language models, generative AI modalities, Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, EMR, SageMaker, DataDog, PagerDuty, data cataloging tools, data observability tools, and data governance tools.
- Strong interpersonal, verbal, and written communication skills and ability to work effectively in a remote environment.
Benefits
- Comprehensive benefits package including paid time off, medical, dental, and vision insurance, and 401(k) for eligible employees.
- Eligible to participate in long-term incentive programs.
- Remote work environment is supported.
- Travel is required 10% of the time.
Tech Stack
Amazon DynamoDBApache AirflowApache KafkaAWSDatadogdbtDockerKerasKubernetesPandasPythonPyTorchscikit-learnSnowflakeSQLTensorFlow