PyTorch AI Jobs

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Deploy, maintain, and optimize production and research compute clusters. Design and implement scalable and efficient ML inference solutions. Develop dynamic and heterogeneous compute solutions for balancing research and production needs. Contribute to productizing model APIs for external use. Develop infrastructure observability and monitoring solutions.

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Conduct a comprehensive review of existing machine learning methods for state estimation and sensor fusion; develop and implement various algorithms based on the literature review and project requirements using simulated and real-world flight data; assess and compare the performance and computational overhead of the developed algorithms with classical baselines; document methodologies, results, and conclusions; actively participate in flight test sessions to gather real-world data and validate the effectiveness of the developed algorithms in operational conditions; contribute to real-time deployment.

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The Technical Director of AI Safety is responsible for owning the technical strategy for AI Safety by determining research directions and building technologies that mitigate risks from alignment to societal harms. The role leads a high-performing R&D team through intentional hiring, mentorship, and cultivation of a culture defined by technical excellence and high output. It involves driving academic impact by guiding complex machine learning projects and securing top-tier publications to establish Faculty's reputation in the AI safety domain. The position shapes market-leading offerings for frontier labs and security institutes by translating cutting-edge R&D into practical safety solutions. The role oversees technical delivery of AI safety and security projects, ensuring scientific rigor and high-quality outputs across evaluations and red-teaming efforts. Additionally, the Technical Director will represent Faculty externally as a primary technical voice, delivering thought leadership and speaking at major global industry events. The role includes collaboration with business unit directors and commercial teams to align research investments with strategic growth and client needs, as well as the opportunity to hire and build a world-class AI safety technical team, design and lead an AI safety R&D program, build scaling work with Frontier Labs, and contribute to the international debate on AI safety including working with governments and other key bodies.

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As an Applied AI Engineer at Snorkel, you will research and utilize state-of-the-art generative AI and machine learning techniques to deliver solutions to customers. Responsibilities include partnering with customers from use case scoping and data exploration to model development and deployment, using Snorkel Flow or custom approaches to provide real business value. You will develop and implement AI systems such as retrieval-augmented generation, fine-tuning pipelines, prompt engineering recipes, and agentic workflows. The role involves creating augmented datasets and evaluation workflows to ensure model reliability and transparency, managing relationships with customer leadership and stakeholders, and collaborating with pre-sales Solutions and Product teams to align customer needs with platform capabilities. You will work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices, lead stakeholder education on AI capabilities, represent customer feedback to product teams, and conduct enablement workshops for customers. The position requires up to 25% annual travel.

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The role involves leading the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. It includes exploring the intersection of computer vision and robotic control to design systems that allow robots to perceive and interact with objects in dynamic environments. Responsibilities include creating models that integrate visual data to guide physical manipulation, collaborating with a multidisciplinary team to translate concepts into deployable robotic capabilities, researching and developing deep learning architectures for visual perception and sensorimotor control, designing algorithms for manipulating complex or deformable objects with precision, optimizing and deploying prototypes onto robotic hardware, evaluating model performance in simulation and real-world environments for robustness, identifying opportunities to apply advancements in computer vision and robot learning to industrial problems, and mentoring junior researchers while contributing to the technical direction of the research roadmap.

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Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Explore the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. Collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities that can be deployed on physical hardware for industrial applications. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms that enable robots to manipulate complex or deformable objects with high precision. Collaborate with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in both simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply state-of-the-art advancements in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.

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Develop and optimize computer vision algorithms for object detection, tracking, and 3D reconstruction. Process and analyze large-scale point cloud data for digital twin applications. Support the integration of robotics frameworks such as ROS into real-time systems. Test and improve system performance in diverse environments. Collaborate with a multidisciplinary team to prototype and deploy innovative solutions.

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As a Machine Learning Engineer in OpenAI's Applied Group on the Integrity team, you will design and deploy advanced machine learning models that solve real-world problems, bringing OpenAI's research from concept to implementation and creating AI-driven applications with a direct impact. You will work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Responsibilities include implementing scalable data pipelines, optimizing models for performance and accuracy, ensuring they are production-ready, staying current with the latest developments in machine learning and AI, participating in code reviews, sharing knowledge, leading by example to maintain high-quality engineering practices, and monitoring and maintaining deployed models to ensure continued value delivery.

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As a Data Science AI Trainer, you will design original computational data science problems simulating real-world analytical workflows across various industries such as telecom, finance, government, e-commerce, and healthcare. You will create problems requiring Python programming (using libraries such as pandas, numpy, scipy, sklearn, statsmodels, matplotlib, seaborn) that are computationally intensive and cannot be solved manually within reasonable timeframes. You will develop problems involving non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Your tasks also include creating deterministic problems with reproducible answers, basing problems on real business challenges like customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency, and designing end-to-end problems covering the full data science pipeline from data ingestion through deployment considerations. You will incorporate big data processing scenarios requiring scalable computational approaches, verify solutions using Python and standard data science libraries, and document problem statements clearly with realistic business contexts along with verified correct answers.

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As a Data Science AI Trainer, the role involves designing original computational data science problems that simulate real-world analytical workflows across various industries such as telecom, finance, government, e-commerce, and healthcare. You will create problems requiring Python programming solutions using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn. The problems must be computationally intensive, non-trivial with reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Problems should be deterministic with reproducible answers by avoiding stochastic elements or using fixed random seeds. The tasks involve basing problems on real business challenges, designing end-to-end problems covering the complete data science pipeline from data ingestion to deployment considerations, incorporating big data processing scenarios requiring scalable approaches, verifying solutions using Python with standard data science libraries and statistical methods, and documenting problem statements clearly with realistic business contexts and verified correct answers.