Senior Machine Learning Engineer
GBG Plc
full-time
Posted on:
Location Type: Hybrid
Location: Manchester • New Hampshire • United States
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Job Level
Tech Stack
About the role
- Design, implement, and optimize state‑of‑the‑art machine learning and computer vision models to enhance product capabilities.
- Research, evaluate, and apply modern architectures and techniques, including CNNs, transformers, and vision‑language models.
- Implement and benchmark newly developed algorithms on large‑scale datasets, validating both accuracy and throughput.
- Fine‑tune large‑scale models using efficient adaptation techniques such as LoRA and QLoRA.
- Define, implement, and monitor appropriate evaluation metrics (e.g., precision, recall, ROC‑AUC, confusion matrices).
- Analyze training, test, and production data using statistical and visual techniques to identify performance gaps and reliability risks.
- Propose and implement data‑driven enhancements to model accuracy, robustness, and system stability.
- Support end‑to‑end ML workflows, including data preparation, training, deployment, monitoring, and iterative improvement.
- Contribute to CI/CD pipelines and production monitoring to ensure reliable, reproducible, and scalable model delivery.
- Assist in diagnosing and resolving model performance regressions and production issues.
- Mentor and support junior CVML engineers across all phases of ML projects, including planning, data collection, annotation, training, deployment, and iteration.
- Participate in design reviews, technical discussions, and knowledge‑sharing initiatives to raise overall team capability.
- Contribute actively to Agile ceremonies and collaborative problem‑solving efforts.
- Proactively suggest improvements to existing models, workflows, tools, and product features.
- Collaborate effectively with engineering, product, and data stakeholders to deliver high‑impact ML solutions.
- Maintain awareness of emerging ML and computer vision trends and assess their applicability to real‑world problems.
Requirements
- Bachelor’s degree or higher in Computer Science, Electrical Engineering, or a related field or equivalent experience
- Strong hands‑on experience developing and deploying machine learning models in production environments.
- Advanced understanding of supervised, unsupervised, and semi‑supervised learning techniques.
- Expertise in classification, regression, clustering, and anomaly detection.
- Solid experience with convolutional neural networks, recurrent neural networks, and transformer‑based models.
- Strong proficiency in Python (C++ is a plus) and PyTorch (TensorFlow is a plus)
- Hands-on experience with modern neural network architectures and loss functions across tasks such as object detection, image segmentation, and representation learning.
- Experience using computer vision and scientific computing libraries such as OpenCV.
- Familiarity with model deployment, monitoring, and CI/CD workflows.
- Beneficial to have experience working with large‑scale datasets and performance‑critical ML systems.
- Prior experience mentoring or technically guiding other ML engineers.
- Beneficial to have exposure to production MLOps practices and model lifecycle management.
- Able to balances research‑driven exploration with pragmatic, production‑focused execution.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningcomputer visionCNNstransformersLoRAQLoRAprecisionrecallROC-AUCPython
Soft Skills
mentoringcollaborationproblem-solvingcommunicationteam capability enhancementagile methodologydata-driven decision makingtechnical guidanceperformance analysisadaptability
Certifications
Bachelor’s degree in Computer ScienceBachelor’s degree in Electrical Engineeringrelated field degree