Machine Learning Engineer

Summary

Lifelancer is a talent-hiring platform that connects professionals with opportunities in various domains. They are seeking a Machine Learning Engineer to own components of machine learning systems, translate requirements into code, and collaborate with cross-functional teams to deliver AI solutions.

Responsibilities

• Own small to medium components of machine learning systems from technical design through implementation and delivery
• Translate technical requirements into high-quality, maintainable code and deliver workstreams according to plan
• Build and maintain data pipelines and feature engineering workflows to support machine learning and AI solutions
• Design, train, evaluate, and refine machine learning models with minimal supervision, applying sound statistical and engineering practices
• Implement ML solutions that can be deployed into production environments as microservices, APIs, batch jobs, or streaming components
• Support production monitoring efforts by helping define and implement metrics for model performance, data drift, anomalies, and retraining triggers
• Collaborate with Data Engineers, Software Engineers, Data Scientists, Product partners, and business stakeholders to deliver project objectives
• Understand system design, data models, and technical artifacts well enough to contribute to implementation decisions and tradeoffs
• Follow governance, documentation, coding, and source control standards consistently
• Demonstrate flexibility and proactively support teammates with day-to-day responsibilities as needed
• Clearly document and communicate work progress, technical decisions, and outcomes to technical and non-technical audiences

Skills

• Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or other quantitative field
• 3+ years of practical experience building, evaluating, scaling, and deploying machine learning pipelines with Python
• Strong programming skills in Python and solid understanding of core computer science principles
• Experience with data manipulation frameworks such as Pandas and PySpark
• Experience with machine learning libraries such as scikit-learn, HuggingFace, TensorFlow/Keras, PyTorch, or MLlib
• Experience with MLOps practices such as automated model deployment, model performance monitoring, data drift detection
• Working knowledge of SQL and relational data structures
• Ability to design, train, and evaluate machine learning models using standard best practices such as model selection, validation, bias/variance tradeoffs, and performance assessment
• Familiarity with batch and streaming data pipeline concepts such as ETL, ELT, and stream processing
• Experience working with cloud environments, preferably AWS
• Familiarity with technologies such as APIs, microservices, Docker, and Kubernetes
• Strong interpersonal, verbal, and written communication skills
• Ability to work effectively in a remote environment using collaboration tools
• Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science
• Experience with managing and architecting solutions on AWS
• Familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production
• Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, Data Cataloging tools, Data Observability tools and Data Governance tools

Qualifications

Must Haves

• Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or other quantitative field
• 3+ years of practical experience building, evaluating, scaling, and deploying machine learning pipelines with Python
• Strong programming skills in Python and solid understanding of core computer science principles
• Experience with data manipulation frameworks such as Pandas and PySpark
• Experience with machine learning libraries such as scikit-learn, HuggingFace, TensorFlow/Keras, PyTorch, or MLlib
• Experience with MLOps practices such as automated model deployment, model performance monitoring, data drift detection
• Working knowledge of SQL and relational data structures
• Ability to design, train, and evaluate machine learning models using standard best practices such as model selection, validation, bias/variance tradeoffs, and performance assessment
• Familiarity with batch and streaming data pipeline concepts such as ETL, ELT, and stream processing
• Experience working with cloud environments, preferably AWS
• Familiarity with technologies such as APIs, microservices, Docker, and Kubernetes
• Strong interpersonal, verbal, and written communication skills
• Ability to work effectively in a remote environment using collaboration tools

Nice to Haves

• Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science
• Experience with managing and architecting solutions on AWS
• Familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production
• Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, Data Cataloging tools, Data Observability tools and Data Governance tools

Benefits

• Paid time off (vacation, holidays, sick)
• Medical/dental/vision insurance
• 401(k) to eligible employees
• Eligible to participate in our long-term incentive programs