Note: The job is a remote job and is open to candidates in USA. GlobalLogic is a trusted digital engineering partner to the world’s largest companies, and they are seeking a Machine Learning Engineer to work primarily on Google Cloud Platform. The role involves deploying machine learning models, integrating them with production applications, and ensuring quality testing and performance evaluation.
Responsibilities
- Strong foundation in ML inference, deployment, and quality testing
- Demonstrated ability to ramp up quickly on new and unfamiliar tech stacks — this is the single most important trait
- End-to-end problem-solving mindset — can own a problem from model handoff to user-facing behavior
- Core ML knowledge sufficient to benchmark models and collaborate with researchers
- Experience deploying models in cloud environments, ideally GCP
- Exposure to Java or JVM-based systems (model integration happens in Java; deep expertise not required)
- Familiarity with streaming data architectures
- Experience in hybrid cloud/on-prem environments
- Evaluate and benchmark new ML inference frameworks to guide production decisions
- Deploy models to GCP and integrate them into production applications and Java-based streaming pipelines
- Own deployment automation end-to-end — from model handoff through live serving
- Monitor how models behave in production for real end-users
- Design and execute benchmarking, performance testing, and quality testing on ML models
- Perform model sampling to support quality evaluation and researcher feedback loops
- Debug issues across the full stack — from inference layer down to streaming pipelines
- Partner with ML researchers to provide benchmarking feedback and guide inference decisions — requires enough core ML knowledge to have a meaningful technical handshake
- Adapt rapidly to non-standard and evolving tech stacks across hybrid (on-prem + GCP) infrastructure
Skills
- Strong foundation in ML inference, deployment, and quality testing
- Demonstrated ability to ramp up quickly on new and unfamiliar tech stacks — this is the single most important trait
- End-to-end problem-solving mindset — can own a problem from model handoff to user-facing behavior
- Core ML knowledge sufficient to benchmark models and collaborate with researchers
- Experience deploying models in cloud environments, ideally GCP
- Exposure to Java or JVM-based systems (model integration happens in Java; deep expertise not required)
- Familiarity with streaming data architectures
- Experience in hybrid cloud/on-prem environments
- Evaluate and benchmark new ML inference frameworks to guide production decisions
- Deploy models to GCP and integrate them into production applications and Java-based streaming pipelines
- Own deployment automation end-to-end — from model handoff through live serving
- Monitor how models behave in production for real end-users
- Design and execute benchmarking, performance testing, and quality testing on ML models
- Perform model sampling to support quality evaluation and researcher feedback loops
- Debug issues across the full stack — from inference layer down to streaming pipelines
- Partner with ML researchers to provide benchmarking feedback and guide inference decisions — requires enough core ML knowledge to have a meaningful technical handshake
- Adapt rapidly to non-standard and evolving tech stacks across hybrid (on-prem + GCP) infrastructure
- Bachelor's or Master's degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field
Benefits
- Flexible work schedules and opportunities to work from home
- Continuing education classes
- Professional certification and training (technical, soft skills, language, and communication skills)
- Additional learning opportunities through international knowledge exchanges
- Health and life insurance
- Short-term and long-term disability insurance
- Matched contribution 401K plan
- Flexible spending accounts
- PTO and holidays
Company Overview
Company H1B Sponsorship