Python AI Jobs

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Lead the design and implementation of a scalable, high-availability voice infrastructure that replaces legacy systems. Build and refine multi-threaded server frameworks capable of handling thousands of concurrent, real-time audio streams with minimal jitter and latency. Deploy robust ASR > LLM > TTS pipelines that process thousands of calls concurrently. Develop robust logic for handling media streams, ensuring seamless audio data flow between clients and machine learning models. Build advanced monitoring and load-testing tools specifically designed to simulate high-concurrency voice traffic. Partner with Speech Scientists and Research Engineers to integrate state-of-the-art models into a production-ready environment.

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Drive the architectural vision for the GenerativeAgent product by designing and building a highly scalable, multi-agent platform for real-time voice and text customer service experiences across various industries. Act as a technical authority and advisor for multiple engineering teams, develop system design and technical roadmaps, and define communication, state management, and orchestration patterns for multi-agent systems. Design and implement scalable, multi-tenant deployment architectures, own and define system-level SLOs/SLIs focusing on latency, cost-efficiency, and fault tolerance, identify systemic risks with proactive mitigation strategies, partner with Security and Compliance teams to meet regulatory and security requirements, lead post-incident analysis and improvements, and collaborate cross-functionally with Product, Customer Engineering, Site Reliability Engineering, TPMs, and Research to translate business requirements into system designs and productionize ML research. Mentor senior engineers and communicate complex technical concepts to both technical and non-technical stakeholders.

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The Lead/Senior Product Manager is responsible for defining and executing the Analytics/Evals/Governance roadmap with clear sequencing and measurable adoption targets. They must partner deeply with Data Science to productize evaluation methodology, including scoring, calibration, prevention of gaming, and tracking drift. They collaborate with Engineering and Observability teams to standardize telemetry and make it usable in product. They drive a cohesive Agentic Studio UX across Build, Operate, and Improve workflows, including dashboards, drill-downs, investigation flows, alerts, and remediation actions. They establish objective success metrics and instrument them end-to-end for data correctness, timeliness, reliability, and customer impact. The role involves working with Delivery/CS and enterprise partners to ensure analytics is usable for real operational processes such as incident response, change management, governance reviews, and quarterly business reviews.

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The backend developer will own major feature development and work directly with founders on product development from end to end. Responsibilities include working with a small interdisciplinary team across hardware, software, and design to build new products from scratch; building new features and designing new architecture to address challenging problems; building backend infrastructure to perform scalable training in the cloud; rethinking and refactoring existing codebases for scale; and continuously improving and maintaining code in production. The role involves full ownership throughout the entire product lifecycle, including idea generation, design, prototyping, execution, and shipping, contributing to multiple parts of the codebase in various programming languages.

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As a Machine Learning Engineer at Noetica, you will build ML models and pipelines with scalability and reproducibility as foundational principles, develop NLP systems that can accurately process and understand complex legal language and terminology, and design and implement LLM-based solutions that are well-documented and empower legal professionals to extract valuable insights. You will extend and create reliable model evaluation frameworks to ensure accuracy and reduce model drift or bias, simplify complex ML systems into more manageable solutions, optimize model performance through smart feature engineering and efficient algorithm selection based on actual use cases, and work with security engineers to implement responsible AI practices that protect sensitive data while delivering valuable insights.

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Define and refine security workflows and incident response strategies. Design and implement advanced security use-cases. Build and automate simulations of complex attack scenarios and environments. Research security incidents and provide insights to enhance AI agents. Collaborate with cross-functional teams to integrate security solutions into the platform.

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The responsibilities include shaping the evolution of Codex by identifying how teams use and break AI-powered software engineering, driving changes across product, infrastructure, and model behavior to improve reliability. Building core team and enterprise primitives to enable Codex usability at scale, such as container orchestration, virtual machine provisioning/configuration, execution sandboxes, shared block storage, RBAC, admin and audit surfaces, usage and pricing controls, managed configuration and constraints, and analytics for visibility into Codex usage. Designing and owning secure, observable, full-stack systems that power Codex across web, IDEs, CLI, and CI/CD, integrating with enterprise identity and governance systems (SSO/SAML/OIDC, SCIM, policy enforcement), and developing data-access patterns that are performant, compliant, and trustworthy. Leading real-world deployments and launches by working with customers and go-to-market teams to roll out Codex across teams, using live usage and operational signals to iterate and improve the product and platform based on real-world feedback.

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As a Principal Software Architect at HackerOne, you will define and evolve the architectural vision for the HackerOne Platform and core systems to ensure scalability, reliability, and performance. You will partner with Product, Platform, and Security teams to translate long-term business and product goals into actionable architectural strategies. You will collaborate with Principal and Distinguished Engineers to align on technical direction, establish shared standards, and evolve system design principles. Your role involves leading major cross-team initiatives to modernize architecture, improve observability, and reduce complexity. You will mentor and guide engineering teams to foster technical excellence, knowledge sharing, and continuous improvement. You will evaluate and integrate emerging technologies including AI, GenAI, and LLM-driven architectures to enhance platform intelligence. Additionally, you will drive architectural governance and documentation to ensure maintainability and transparency and communicate architectural direction clearly to technical and non-technical stakeholders. Within the first 30-90 days, you will move from understanding systems and architecture to leading architectural initiatives that impact teams company-wide.

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Develop state-of-the-art tools for correcting, improving, and enhancing written English using various NLP, ML, and DL technologies. Productize and ship these features into Superhuman's product offerings, which millions of users use daily. Stay up-to-date with the latest research trends that could improve the product. Contribute to the research strategy and technical culture of the company. Attract professionals in the industry to build a best-in-class research team that creates a state-of-the-art writing and communication assistant.

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As an AI Tutor in Coding specializing in Kotlin development, the responsibilities include designing high-quality technical content, examples, and explanations demonstrating best practices in Kotlin development; collaborating with engineers to ensure accuracy and consistency across code samples, tutorials, and developer guides; exploring modern Kotlin frameworks and tools to create practical, real-world examples for learning and testing; and continuously refining content based on feedback, emerging patterns, and advances in the Kotlin ecosystem. The role also involves contributing to projects aligned with skills by creating training prompts and refining model responses to help shape the future of AI while ensuring technology benefits everyone.