Develop advanced machine learning solutions for content understanding across audio, text, and multimodal sources.
Responsibilities
Design, develop, deploy, and improve machine learning models that support content understanding across audio, text, and multimodal content sources.
Apply modern machine learning techniques, including deep learning, transformer architectures, large language models, and multimodal learning approaches to solve content understanding challenges.
Own complex machine learning projects from problem definition through model development, deployment, evaluation, and ongoing optimization.
Evaluate model performance using appropriate offline metrics, experimentation frameworks, and business outcomes to drive continuous improvement.
Collaborate closely with Product, Engineering, Analytics, and Data teams to identify opportunities and deliver impactful machine learning solutions.
Senior Staff Machine Learning Engineer (ML Platform) responsible for building and improving the ML platform infrastructure, tooling, and standards to enable rapid deployment, monitoring, and iteration of ML models across pricing, forecasting, and optimisation.
Responsibilities
Build systems for deploying, iterating, and scaling ML and optimisation models
Provide operational visibility into model performance
Develop evaluation and backtesting tooling for ML models, pricing and trading strategies
Own model inference and training in production, including on‑call during working hours
Lead the development and execution of AI and machine learning strategy for biometric face verification and security.
Compensation
Comp: Negotiable (Base) + Company Performance Bonus (20%) + EMI Share Options + UK iProov Benefits
Responsibilities
Define and steward the AI strategy, roadmap, and vision across core AI research, product, and production domains
Lead teams across research, production/engineering, and MLOps to deliver robust, secure, explainable models for liveness assurance and fraud detection
Oversee model development and deployment pipelines: data ingestion, training, optimization, evaluation, monitoring, and continuous improvement in adversarial biometric threat landscapes
Ensure AI is aligned with product and commercial goals, measuring ROI and outcomes of AI deployments
Drive partnerships and external collaboration with academic, industry, and regulatory stakeholders
Requirements
Master’s or PhD in computer science, machine learning, data science, or a AI related field
Innovative Data Scientist to transform complex data into actionable insights, build predictive models, and leverage ML and AI to solve real-world business challenges for U.S.-based organizations.
Responsibilities
Collect, clean, transform, and analyze structured and unstructured data from multiple sources.
Develop predictive models, machine learning algorithms, and statistical analyses to solve business problems.
Build, validate, and deploy machine learning models for production environments.
Analyze customer behavior, business performance, operational efficiency, and market trends.
Collaborate with Product, Engineering, Business Intelligence, Marketing, and Executive teams to translate business problems into analytical solutions.
Experienced Staff System Test Engineer leading quality efforts across AI-driven embedded and cloud-connected systems, focusing on AI model testing, deployment, monitoring, and test automation.
Compensation
$156,000—$214,000 USD
Responsibilities
Collaborate with diverse teams to define testing strategies for challenging and innovative projects.
Develop and execute detailed test plans using both automated and manual testing.
Triage integration issues, replicate problems, and assist in identifying root causes and solutions.
Escalate potential issues (Software, Hardware, Resources) to the Project Manager/Executive team.
Track testing activities, ensuring clear alignment across key stakeholders.
Lead complex yield analytics and machine learning projects to improve manufacturing yield and reduce cost in Micron’s front‑end memory fabs.
Responsibilities
Work with yield and process engineering to find root causes of yield loss and excursions using wafer, die, inline metrology, defect, equipment (FDC) and electrical test data
Build and deploy ML/AI solutions, including virtual metrology, yield prediction, anomaly and excursion detection, wafer‑map pattern recognition and process optimization
Apply GenAI and agentic AI, exploring and delivering LLM‑ and agent‑based tools that speed up engineering analysis, root‑cause investigation and knowledge reuse
Communicate impact by presenting findings and recommendations to engineers and senior management, quantifying business value
Set technical direction, review others’ work and mentor junior data scientists
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