Python AI Jobs

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Build and maintain features for the web-based property management platform using TypeScript, React, Node.js, PostgreSQL, and AWS. Contribute to a monorepo architecture, working within two-week sprint cycles to deliver high-quality code. Implement integrations including DocuSign, Plaid, Stripe, and ownership group payout systems. Optimize platform performance and user experience by replacing legacy systems. Build and integrate AI agents using Claude and other AI APIs to automate organizational processes, developing API integrations and custom agents. Collaborate with the CEO on prioritizing automation opportunities. Take ownership of tasks, independently research and implement solutions to challenges, proactively identify and implement improvements, and contribute ideas to platform architecture and development priorities.

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Design and build AI agents that outperform human agents in managing complex customer interactions and driving customer retention. Identify cross-customer trends that guide the evolution of Decagon’s agent building platform and research efforts. Experiment with and run evaluations on the latest text and voice models, then integrate them at scale with large enterprise-grade customers.

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Design and build AI agents that outperform human agents in managing complex customer interactions and driving customer retention. Identify cross-customer trends that guide the evolution of Decagon’s agent building platform and research efforts. Experiment with and run evaluations on the latest text and voice models, then integrate them at scale with large enterprise-grade customers. Have complete ownership and autonomy in building and shipping best-in-class AI agents, from initial implementation through continuous iteration, working directly with leaders across industries like finance, healthcare, and hospitality to solve their users’ needs with reliable and intuitive AI agents. Dive deep into complex system challenges and build elegant solutions that scale to millions of users.

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Define and own the roadmap for North's agent harness, including the agent loop, context engineering layer, tool orchestration, sandbox execution, and sub-agent delegation. Serve as the primary interface between North engineering and Cohere's Modeling team, ensuring new harness capabilities are validated before being built and that neither team limits future possibilities. Own North's agentic evaluation framework, ensuring evaluations are compatible with both the North harness and Modeling's training infrastructure, serving as a reliable bridge between product and research. Engage enterprise customers to identify real-world agentic failures and translate findings into product and model requirements. Stay current with the open-source and commercial agent ecosystem and drive adoption decisions that align North's architecture with emerging standards.

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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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Design, build, and optimize the core native runtime powering LM Studio and the C++ libraries powering the app and APIs. Work across runtime, LLM engines, llama.cpp/MLX integrations, build infrastructure, and on-device AI software. Focus on system and library integration by wiring the C++ runtime to GPU backends, vendor SDKs, and operating-system services to support user-facing applications. Implement and harden system-level code involving threading, memory, files, IPC, and scheduling. Integrate platform acceleration paths such as Metal, CUDA, and Vulkan across macOS, Windows, and Linux. Profile, debug, and tune execution paths to ensure fast, dependable local AI and maintainable software. Contribute to the C++ runtime powering LM Studio, extend LLM engine integrations, and build platform-aware performance features for desktop OS. Implement resilient IPC, resource management, and scheduling logic to support concurrent model execution. Improve build, packaging, and release infrastructure for native components. Collaborate with the team to deliver cohesive and recognizable user experiences.

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As a Research Engineer at Mercor, you will own benchmarking pipelines, evaluation systems, and failure analysis workflows that directly inform how frontier language models are trained and improved. You will design, implement, and maintain benchmarks and metrics for tool use, agentic behavior, and real-world reasoning, ensuring they scale with training and align with product and research goals. You will build and operate LLM evaluation systems including runs, scoring, dashboards, and reporting to allow tracking and comparison of model performance at scale. You will conduct systematic failure analysis on model outputs, categorize failure modes, quantify their prevalence, and use these insights to influence reward design, data curation, and benchmark design. Additionally, you will create and refine rubrics, automated evaluators, and scoring frameworks that influence training and evaluation decisions, balancing rigor and scalability. You will quantify data usability and quality, guide data generation, augmentation, and curation based on evaluations and failure analysis. Collaboration with AI researchers, applied AI teams, and data producers to align evaluations with training objectives and prioritize important benchmarks and failure analyses is expected. Finally, you will operate with strong ownership in a fast-paced, high-iteration research environment.

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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.