Location: Philadelphia PA (Onsite 4 days/week at 1800 Arch Street) Alternate location: Reston VA (for strong candidates) Duration: Contract Eligibility: USC GC
Job Summary
We are seeking a hands-on Machine Learning Engineer with 5 years of experience who can design build and deploy scalable machine learning solutions. This role requires strong coding expertise and real-world experience delivering models into production environments. The ideal candidate is not a manager but an individual contributor who thrives in a fast-paced engineering-focused environment.
Key Responsibilities
Model Development: Design build train and fine-tune machine learning and deep learning models for real-world use cases
Production Deployment: Deploy monitor and maintain ML models in production environments
Data Pipeline Development: Build and optimize scalable data pipelines for ingestion transformation and processing
Performance Optimization: Evaluate models using metrics like accuracy recall and AUC; optimize for performance and scalability
Collaboration: Work closely with cross-functional teams including data engineers software engineers and business stakeholders
Required Skills & Qualifications
5 years of experience as a Machine Learning Engineer or similar role
Strong Python programming skills with solid software engineering fundamentals
Recent and hands-on experience with PySpark (mandatory)
Experience with machine learning frameworks such as Scikit-learn
Strong understanding of statistics probability and algorithms
Experience working with SQL data modeling and large datasets
Proven track record of deploying ML models into production environments
Experience with AWS services
Preferred Qualifications
Experience with MLOps tools such as Docker for model deployment
Hands-on experience with local Large Language Models (LLMs)
Familiarity with distributed computing and big data technologies
Interview Process
Round 1 (30 mins Virtual)
Experience overview
Technical discussion
Live coding exercise (Video ON full desktop screen sharing required)
Round 2 (60 mins In-Person Preferred)
Technical deep dive
Advanced live coding exercise
Work Environment
4 days onsite preferred (Philadelphia office)
Open to relocation candidates
Reston VA location may be considered if needed
Job Title: Machine Learning Engineer w2 role Location: Philadelphia PA (Onsite 4 days/week at 1800 Arch Street) Alternate location: Reston VA (for strong candidates) Duration: Contract Eligibility: USC GC Job Summary We are seeking a hands-on Machine Learning Engineer with 5 years of exp...
Job Title: Machine Learning Engineer w2 role
Location: Philadelphia PA (Onsite 4 days/week at 1800 Arch Street) Alternate location: Reston VA (for strong candidates) Duration: Contract Eligibility: USC GC
Job Summary
We are seeking a hands-on Machine Learning Engineer with 5 years of experience who can design build and deploy scalable machine learning solutions. This role requires strong coding expertise and real-world experience delivering models into production environments. The ideal candidate is not a manager but an individual contributor who thrives in a fast-paced engineering-focused environment.
Key Responsibilities
Model Development: Design build train and fine-tune machine learning and deep learning models for real-world use cases
Production Deployment: Deploy monitor and maintain ML models in production environments
Data Pipeline Development: Build and optimize scalable data pipelines for ingestion transformation and processing
Performance Optimization: Evaluate models using metrics like accuracy recall and AUC; optimize for performance and scalability
Collaboration: Work closely with cross-functional teams including data engineers software engineers and business stakeholders
Required Skills & Qualifications
5 years of experience as a Machine Learning Engineer or similar role
Strong Python programming skills with solid software engineering fundamentals
Recent and hands-on experience with PySpark (mandatory)
Experience with machine learning frameworks such as Scikit-learn
Strong understanding of statistics probability and algorithms
Experience working with SQL data modeling and large datasets
Proven track record of deploying ML models into production environments
Experience with AWS services
Preferred Qualifications
Experience with MLOps tools such as Docker for model deployment
Hands-on experience with local Large Language Models (LLMs)
Familiarity with distributed computing and big data technologies
Interview Process
Round 1 (30 mins Virtual)
Experience overview
Technical discussion
Live coding exercise (Video ON full desktop screen sharing required)