Python AI Jobs

View detail

As a Senior AI Research Scientist for Perception for Contact Rich Manipulation, you will lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. You will explore the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments. Your work will involve creating models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. You will collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities deployable on physical hardware for industrial applications. Responsibilities include researching and developing deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios; designing algorithms to enable robots to manipulate complex or deformable objects with high precision; collaborating with software engineers to optimize and deploy research prototypes onto physical robotic hardware; evaluating model performance in simulation and real-world environments to ensure robustness and reliability; identifying opportunities to apply state-of-the-art advancements in computer vision and robot learning to practical industrial problems; mentoring junior researchers and contributing to the technical direction of the manipulation research roadmap.

View detail

As a Senior AI Research Scientist for Perception for Contact Rich Manipulation, you will lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. You will explore the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments. Your work will involve creating models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. You will 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. Specifically, you will research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios, design algorithms to 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, and mentor junior researchers while contributing to the technical direction of the manipulation research roadmap.

View detail

As an Aerodynamics Methodology & Software Engineer at Harmattan, the responsibilities include professionalizing internal computational ecosystem software by refactoring research scripts and specialist tools into modular, high-performance, and maintainable Python/C++ libraries with robust unit-testing and documentation standards; ensuring team adherence to code structure. Architect agentic AI workflows and custom MCP servers integrating LLMs with internal CFD solvers and databases to automate simulation setup, code refactoring, and data analysis. Develop APIs and automated workflows linking tools like OpenVSP, XFoil, and OpenFOAM for seamless optimization loops in MDAO toolchain integration. Manage and optimize HPC and Cloud computing infrastructure including Linux-based HPC clusters (Slurm) and cloud platforms such as AWS and Azure. Design and manage the aerodynamic database architecture for high-dimensional aerodynamic result storage and retrieval, providing single-source-of-truth access to vehicle performance data for GNC and flight physics teams.

View detail

Design and implement evaluation pipelines to measure the performance and reliability of AI models. Develop automated testing frameworks to assess model outputs at scale. Analyze model performance using both traditional statistical metrics and AI-specific evaluation methods. Evaluate AI systems built on modern architectures such as LLM-based applications and Retrieval-Augmented Generation (RAG). Identify potential issues related to accuracy, hallucinations, bias, safety, and model drift. Conduct adversarial testing to uncover vulnerabilities and ensure safe model behavior. Collaborate with engineering and AI teams to improve prompt design, model outputs, and system performance. Monitor model performance in production and help define best practices for AI evaluation and observability.

View detail

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.

View detail

As a Machine Learning Engineer on the Integrity team at OpenAI, the responsibilities include designing and deploying advanced machine learning models that address real-world problems, specifically to maintain the security and integrity of OpenAI's platforms against adversarial threats such as financial abuse and scaled attacks. The role involves collaborating closely with researchers, software engineers, and product managers to solve complex business challenges and deliver AI-powered solutions that improve the trust and safety of the platform. Engineers are expected to implement scalable data pipelines, optimize models for performance and accuracy, and ensure models are production-ready. They also monitor and maintain deployed models to ensure they continue delivering value. Additionally, the role includes engaging with the latest developments in machine learning and AI, participating in code reviews, sharing knowledge, and leading by example to uphold high-quality engineering practices.

View detail

As an Engineering Leader at Ema, you will build and lead a high-performance engineering organization by recruiting, hiring, and developing senior engineers across multiple sub-teams such as cloud infrastructure, data platform, ML operations, and developer experience. You will establish engineering standards, code review culture, on-call expectations, and a bias-toward-shipping mentality balanced with production rigor. You will coach and grow senior/staff engineers into technical leaders and manage engineering managers as the organization scales. Additionally, you will set the 6–18 month platform roadmap in partnership with engineering teams, make high-stakes architectural decisions regarding build vs. buy, migration strategies, and technology bets, and own the outcomes. You will drive cross-functional alignment with product, ML/AI research, and go-to-market teams ensuring the platform evolves with customer and product needs. Moreover, you will own production health for all platform services including incident response, postmortems, SLO tracking, and capacity planning. You will establish and iterate on engineering practices that enable fast shipping without compromising reliability and participate in executive-level reviews on infrastructure spend, system health, and engineering velocity.

View detail

As a Data Scientist in Safety Systems, you will establish the data-driven approach for understanding, evaluating, and monitoring the safety of OpenAI's production systems. You will collaborate with partners across the company to define north-star metrics, own and implement statistical methods to productionize those metrics, conduct analysis to understand the impact of products, and establish source-of-truth dashboards for safety-related questions. Your role includes leading efforts to understand and measure real-world safety impacts of products, uncovering new ways to measure and mitigate harm and abuse, developing and implementing necessary statistical methods, providing direction and coordination of projects, establishing a data-driven culture by defining and tracking metrics at feature, product, and company levels, creating and disseminating dashboards and reports, and developing a safety data flywheel to provide production insights and data for training and evaluation.

View detail

As a Data Science AI Trainer at Mindrift, your responsibilities will include 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 that require programming in Python using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn. The problems must be computationally intensive and not solvable manually within reasonable timeframes, requiring non-trivial reasoning in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. You will ensure the problems are deterministic with reproducible answers, based on real business challenges such as customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency. The design will cover end-to-end data science pipelines from data ingestion to deployment considerations, including big data processing scenarios with scalable computational approaches. You will verify solutions using Python and standard data science libraries and document problem statements clearly with realistic business contexts and verified correct answers.

View detail

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 that require Python programming using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn. These problems must be computationally intensive, requiring non-trivial reasoning in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. You will ensure problems are deterministic with reproducible answers by avoiding stochastic elements or using fixed random seeds. The problems will be based on real business challenges like customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency, covering end-to-end data science pipelines from data ingestion through deployment considerations. Your tasks will also include incorporating big data processing scenarios, verifying solutions using Python and standard data science libraries, and documenting problem statements clearly with realistic business contexts, providing verified correct answers.