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This role involves architecting and optimizing end-to-end training workflows for robotics foundation models like Cosmos and GR00T. The candidate will work with researchers and engineers to scale multimodal model training and data pipelines across multi-GPU and multi-node systems.
Develops AI/ML solutions (predictive models, generative AI, agentic systems) for government case management modernization, focusing on compliance, MLOps, and data-driven decision tools.
This is a pre-sales technical role focused on designing and presenting cloud-native data engineering and streaming architectures to enterprise clients. The specialist will lead Proofs of Concept, influence product roadmaps, and provide deep expertise in technologies like Apache Spark, Kafka, and Flink to support AI-ready data foundations.
Lead Data Scientist translates business challenges into data-driven insights by building, deploying, and optimizing ML models (including generative AI) using Python/R/Scala. Focuses on full AI workflows—from feature engineering to model monitoring—while mentoring teams and advising executives on strategic decisions.
Develops and operationalizes advanced machine learning and statistical solutions to improve pharmaceutical development and manufacturing processes, collaborating with lab scientists and engineers to deliver scalable, data-driven applications.
Principal Software Engineer focused on High Performance Computing for AI/ML model training and inference, using Python, C/C++, PyTorch, and GPU accelerators at JPMorganChase.
Lead the modeling strategy and analytic architecture for telemetry and log analytics at Dell, focusing on time-series and event-based data across batch and streaming systems. Design models for anomaly detection, forecasting, and NLP on unstructured logs using Python, SQL, and observability ecosystems.
AI Architect leading Agentic AI/GenAI initiatives at Dell, owning the full AI/ML lifecycle from prototype to deployment, designing autonomous multi-agent systems, and transforming customer support. Core technologies include Python, LLMs, Docker/Kubernetes, and cloud platforms (AWS/GCP/Azure).
The SEO Data Scientist will analyze large-scale data to influence eBay's SEO product roadmap and strategy. The role involves designing A/B tests, building predictive models, and partnering with cross-functional teams to improve marketplace performance.
Research scientist driving applied AI and Generative AI initiatives—foundation models, NLP, large-scale GPU model training—within CIBC's Advanced Analytics and AI team in Toronto.
Quantum Machine Learning Research Scientist at CIBC driving applied research on quantum algorithms for fraud, cybersecurity, and optimization use cases using quantum frameworks like Qiskit, Cirq, Ocean, Q#, and Python.
Data Scientist on Workday's Deployment Technology team, analyzing deployment/migration product performance, building Tableau dashboards, prototyping predictive models in Jupyter, and identifying AI/ML automation opportunities using SQL, statistics, and data warehousing.
As a Data Scientist at P&G, you will design and implement scalable machine learning, optimization, and generative AI models to solve complex business problems. You will collaborate with engineering teams to productionize these solutions while mentoring others and applying advanced analytics to massive datasets.
The Senior Data Scientist will design and implement advanced AI, machine learning, and optimization models to solve complex business challenges at scale. The role involves collaborating with engineering teams to deploy production-ready solutions using cloud platforms like GCP or Azure.
The ML Engineer will train, fine-tune, and deploy Computer Vision models while managing datasets and monitoring production performance. The role requires expertise in Python, deep learning frameworks, and MLOps practices to ensure model accuracy and reliability.
Junior AI/ML engineer developing and deploying ML/DL models, NLP/RAG applications, and LLM solutions for financial institutions using Python, SQL, and MLOps.
Train, evaluate, deploy and monitor computer vision models (detection, classification, tracking) in Singapore, using Python and deep learning frameworks like PyTorch or TensorFlow.
Develop and optimize machine vision systems for inspection, measurement, and defect detection; configure vision hardware/software (cameras, lighting, frame grabbers) and provide technical support using tools like OpenCV, HALCON, Cognex VisionPro with C++, C#, or Python.
PhD researcher (EDB-IPP programme) focused on LLM model compression and acceleration—developing quantization, pruning, knowledge distillation, and inference optimization techniques using PyTorch for large language, multimodal, and diffusion models.
The Machine Learning Engineer Intern will join the Model Engineering Team to train, fine-tune, and optimize deep learning models for video intelligence applications. The role involves hands-on experimentation with CNNs, Transformers, and foundation models to improve real-world vision system performance.
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