Senior Machine Learning Engineer (3968)

GBG•Manchester, NH

About The Position

Enabling safe and rewarding digital lives for genuine people, everywhere We make it our mission to ensure more genuine people have digital access to opportunities, and businesses have access to more genuine people. Our technology draws on diverse and reliable data to create a single point of truth for identity and address verification. With over 30 years of experience behind us our team and technology are focused on enabling safe and rewarding digital lives for everyone. Regardless of age, location or background, genuine people everywhere should be able to digitally prove who they are and where they live. About the team and roleCVML Teams At the heart of GBG's Documents and Biometrics portfolio, our team focuses on creating unique and powerful artificial intelligence models. These models are designed to revolutionize KYC verification for our customers. We drive the development of these cutting-edge technologies, aiming to provide unparalleled solutions for document verification and digital trust. Collaboration is our cornerstone as we bring together diverse expertise to achieve collective success. Guided by Agile methodology, our daily operations focus on efficiency through automation. Senior Machine Learning Engineer The Senior Machine Learning Engineer is a senior individual contributor responsible for designing, developing, deploying, and continuously improving machine learning and computer vision models that power production‑grade systems. This role combines strong hands‑on technical execution with mentorship, collaboration, and data‑driven problem solving. Operating within an Agile environment, the Senior ML Engineer works closely with the machine learning team and cross‑functional partners to translate product requirements into robust ML solutions. The role requires deep expertise in modern ML and computer vision techniques, experience operating models in production, and the ability to guide junior engineers through the full ML lifecycle while driving measurable improvements in model performance and product quality.

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.
Able to balances research‑driven exploration with pragmatic, production‑focused execution.

Nice To Haves

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.

Responsibilities

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.