Job Requirements
At Quest Global, it's not just what we do but how and why we do it that makes us different. With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. Our people are driven by the desire to make the world a better placeto make a positive difference that contributes to a brighter future. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster.
Key Responsibilities
- Clinical Imaging AI: Design and implement ML/DL pipelines for MR imaging tasks (segmentation, classification, anomaly detection), including quantitative image analysis.
- Data & DICOM: Build preprocessing pipelines for MR and 3D DICOM data; implement reliable data ingestion, parsing, deidentification, and curation across structured, unstructured, audio, static, and streaming datasets.
- Model Excellence: Ensure model explainability, statistical rigor, bias checks, and robust validation (crosssite, crossscanner) in line with clinical and regulatory/quality expectations.
- Foundation & Vision Models: Finetune and adapt modern vision and multimodal models (e.g., Vision Transformers, CLIP, MedSAM, BioGPT or similar) for diagnostic and clinical applications.
- GenAI & LLMs: Lead initiatives in Generative AI and LLMs (pretraining, finetuning, domain adaptation, agentbased workflows) for imagingcentric and multimodal solutions; integrate NLP/NLU for clinical text and reporting.
- Production & Cloud: Operationalize models on AWS/Azure; contribute to MLOps (versioning, CI/CD, monitoring, drift detection, rollback) with secure, compliant deployments.
- Experimentation & Research: Stay current with ML/GenAI research; evaluate SOTA methods and translate promising approaches into productionready features.
- Stakeholder Engagement: Translate business challenges into ML problem statements; articulate design decisions, tradeoffs, and outcomes to clinical, product, and business stakeholders.
- Quality & Risk: Partner with QA/RA teams on documentation, validation protocols, and audit readiness for clinical use.
- Collaboration: Work closely with radiology experts and imaging engineers to align solutions with clinical workflows and radiology practices, particularly for MR.
We are known for our extraordinary people who make the impossible possible every day. Questians are driven by hunger, humility, and aspiration. We believe that our company culture is the key to our ability to make a true difference in every industry we reach. Our teams regularly invest time and dedicated effort into internal culture work, ensuring that all voices are heard.
We wholeheartedly believe in the diversity of thought that comes with fostering a culture rooted in respect, where everyone belongs, is valued, and feels inspired to share their ideas. We know embracing our unique differences makes us better, and that solving the worlds hardest engineering problems requires diverse ideas, perspectives, and backgrounds. We shine the brightest when we tap into the many dimensions that thrive across over 21,000 difference-makers in our workplace.
Work Experience
Education & Experience (either path):
- PhD in CS/EE/Biomedical/Medical Imaging/Data Science or related field and 5+ years of postdegree experience in AI/ML for medical imaging; or
- M.Tech in CS/EE/Biomedical/Medical Imaging/Data Science or related field and 8+ years of relevant experience.
Required Skillset
- Deep domain knowledge of medical imaging modalities, MR physics/workflows, and radiology practices.
- Advanced Python programming; strong in TensorFlow and/or PyTorch; familiarity with medical image processing tools/libraries.
- Proven track record deploying clinicalgrade AI/ML solutions with explainability, validation, and regulatory/quality considerations.
- Handson DICOM parsing, annotation tooling, clinical data curation/management.
- Solid statistical modeling, feature engineering, and applied ML for imaging data.
- Practical data augmentation and preprocessing strategies for MR and other modalities; rigorous validation methodologies.
- Experience developing and deploying ML on AWS or Azure.
- Advanced proficiency in NLP/NLU.
- Strong command of Generative AI and LLMs (pretraining, finetuning, domain adaptation, agentbased workflow design).
- Ability to convert business challenges into ML problem statements and ship endtoend solutions.
- Clear communication with both technical and business stakeholders.
Good to have
- Experience with foundation/vision models (e.g., Vision Transformers, CLIP, MedSAM, BioGPT, or similar multimodal models) for diagnostic imaging.
- Exposure to IEC 62304, ISO 13485, HIPAA/GDPR, and healthcare data security practices.
- Familiarity with clinical data standards (e.g., HL7/FHIR) and PACS/RIS integration.
- MLOps tooling (MLFlow, Weights & Biases, Kubernetes, Docker) and production monitoring for imaging AI.
- Publications or patents in medical imaging AI; contributions to opensource imaging or ML frameworks.
