Base: $86,000.00 to $135,000.00; bonus/equity: dis...
Hybrid
2+ years production ml deployment experience
Proficiency in aws, gcp, or azure cloud platforms
Experience with tensorflow, pytorch, or scikit-learn
GN Group is seeking a Machine Learning Engineer for a hybrid role focused on developing cloud-based solutions for their Jabra Perform and BlueParrott product lines. The ideal candidate will have experience deploying machine learning models in production environments and a strong background in relevant programming languages and frameworks
Job Summary
This hybrid role requires candidates to reside within a commutable distance of offices in Dover, NH, or Lowell, MA.
The team focuses on developing Jabra Perform and BlueParrott product lines to enhance tools for frontline workers.
Compensation ranges from $86,000.00 to $135,000.00 annually with eligibility for discretionary bonuses and a competitive benefits package.
Matching Summary
Match Score: 85
GN Group is seeking a Machine Learning Engineer for a hybrid role focused on developing cloud-based solutions for their Jabra Perform and BlueParrott product lines. The ideal candidate will have experience deploying machine learning models in production environments and a strong background in relevant programming languages and frameworks.
Salary
Base: $86,000.00 to $135,000.00; Bonus/Equity: Discretionary bonus eligible; Benefits: Health insurance, 401(k), paid time off
Skills & Requirements
Must-have
2+ years production ML deployment experience
Proficiency in AWS, GCP, or Azure cloud platforms
Experience with TensorFlow, PyTorch, or Scikit-learn
Strong Python programming skills for ML algorithms
Knowledge of big data technologies and distributed computing
Nice-to-have
Fundamentals of audio and speech signal processing
Collaborative mindset across technical disciplines
Experience transitioning research to production models
Key Requirements
2+ years experience deploying ML models in production
Proficiency in Python, R, Java, or C++
Familiarity with large dataset processing frameworks