The Manufacturing Data Analyst supports Factory of the Future and Industry 4.0 initiatives by leveraging data, analytics, and AI technologies to improve manufacturing efficiency, quality, and operational performance.
Compensation
$90,000 - $125,000 annual compensation
Responsibilities
Lead industry 4.0 and AI initiatives by identifying, prioritizing, and implementing data-driven solutions that optimize manufacturing processes across molding, assembly, and co-extrusion processes.
Collaborate with cross-functional teams including engineering, operations, IT, and automation teams to integrate digital technologies and analytics into production systems
Develop and maintain data infrastructure including ETL pipelines, databases, SQL queries, and data environments to support manufacturing analytics and AI applications.
Collect, validate, and manage production data from multiple sources, including PLCs, HMIs, sensors, and enterprise systems, ensuring data accuracy and integrity.
Design, develop, and optimize machine learning and AI models to improve quality, productivity, predictive maintenance, process control, and operational efficiency.
Requirements
Bachelor's degree in a relevant field such as Computer Science, Engineering, Data Science, or a related discipline.
Nice to have: familiarity with cloud-based analytics platforms
Design and develop advanced AI algorithms for next-generation video analytics, leveraging modern AI models and architectures to solve complex video understanding challenges.
Responsibilities
Research and explore state-of-the-art AI models, Computer Vision techniques, and emerging technologies to identify new opportunities for advancing video analytics capabilities.
Develop algorithms and solutions for challenging video analytics and video understanding problems, leveraging Computer Vision, deep learning, LLMs, VLMs, and other relevant AI technologies.
Experiment and evaluate different models, algorithms, and approaches, build proof-of-concepts, and assess their performance and suitability for real-world applications.
Integrate algorithms and AI models into existing software systems, collaborating with software engineers to deliver complete product capabilities.
Optimize algorithms and model inference for performance, accuracy, and resource utilization, meeting production requirements and hardware constraints.
Requirements
Master's degree in Computer Science, mathematics, EE or a related field
Responsible for designing and implementing specific architectural components and solutions for AI agents within the Bluefield framework, collaborating with Generative AI Engineers.
Compensation
Salary Range: $175,860.00 - $214,940.00
Responsibilities
Design detailed technical solutions and blueprints for individual AI agents, including data flows, API integrations, and component interactions.
Ensure agent development efforts strictly follow established architectural patterns, coding standards, security protocols, and best practices.
Optimize Google Agent Space and Google Cloud Platform services for agent performance, cost-efficiency, and scalability.
Design and contribute to the developpment of reusable agentic components, libraries, and common services.
Provide expert-level troubleshooting for complex architectural and integration challenges.
Requirements
Bachelor's degree in Computer Science
Data Science
related field, or equivalent practical experience
Nice to have: Healthcare payer domain
Nice to have: Solution Architect Associate certification
Build and scale training libraries and infrastructure for autonomous driving models, enabling training on thousands of GPUs and massive datasets.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 224,000 USD – 356,500 USD for Level 5; and 272,000 USD – 431,250 USD for Level 6.
Responsibilities
Crafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
Improving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
Building robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
Collaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
Owning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
Researcher in LLM reliability and uncertainty for AI science assistants
Compensation
Annual Salary Range: $44,500 - $71,200
Responsibilities
Research and evaluate uncertainty quantification and hallucination detection methods for multi-turn, agentic scientific workflows
Develop probing methods that predict, from a model's internal representations, when a scientific task specification is incomplete or inconsistent and a clarifying question is warranted
Build and instrument evaluation pipelines that capture and analyze model internal states over multi-turn scientific dialogue on HPC systems
Conduct experiments and analyze model behavior across computational science domains and established benchmarks
Contribute to technical documentation, research reports, publications, and presentations summarizing project progress and findings
Build and scale deep learning infrastructure libraries and frameworks for autonomous driving models.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 224,000 USD – 356,500 USD for Level 5; and 272,000 USD – 431,250 USD for Level 6.
Responsibilities
Crafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
Improving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
Building robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
Collaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
Owning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
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