Prospera AI - AI Backend Engineer
The AI Backend Engineer will own and evolve the LLM orchestration pipeline, including designing and optimizing the multi-agent orchestration system, implementing parallelization and streaming to reduce response latency, and building prompt management with versioning and A/B testing. They will design retrieval-augmented generation (RAG) systems for accurate contextual responses, work with vector databases, embeddings, and relevance scoring, and optimize for speed and accuracy at scale. The role involves developing production APIs to connect AI capabilities to the frontend, with considerations for authentication, rate limiting, documentation, and designing for future integrations with CRMs and advisor tools. Additionally, the engineer will establish code review practices and testing standards, document architecture decisions, and contribute to technical patents and intellectual property development.
Full Stack AI Engineer – BuilderEx
Design, build, and maintain full-stack applications powering identity and access management (IAM) experiences. Develop and integrate AI/ML models for identity use cases such as fraud detection, anomaly detection, risk-based authentication, and identity verification. Lead and execute SSO migrations across products and platforms, consolidating authentication flows while minimizing user disruption. Drive domain consolidation initiatives by unifying identity systems, services, and user data models across multiple platforms or brands. Improve developer experience by building internal tools, SDKs, APIs, and documentation that simplify identity integrations. Design and evolve secure, scalable APIs supporting authentication, authorization, and identity data services. Partner with Security, Platform, and Product teams to implement and standardize protocols and patterns such as OAuth 2.0, OpenID Connect, SAML, JWT, and zero-trust architectures. Ensure AI-powered identity systems are observable, explainable, and production-ready with robust monitoring and feedback loops. Balance security, performance, and usability while maintaining privacy and compliance. Contribute to architectural decisions, technical design discussions, and code quality standards.
Full Stack AI Engineer
Design, build, and deploy AI/ML solutions to automate ITSM ticket triage, classification, prioritization, and routing. Develop NLP-based models for ticket summarization, root-cause detection, and resolution recommendation. Implement AI-powered virtual agents / copilots to assist support engineers and end users. Partner with Product Support, SRE, and Engineering teams to understand recurring issues and automate resolution workflows. Build intelligent runbooks and self-healing automation for common incidents and service requests. Enhance knowledge management by auto-generating and updating KB articles from resolved tickets. Integrate AI solutions with ITSM platforms (HALO). Develop APIs, workflows, and event-driven automations across monitoring, logging, and ITSM tools. Ensure seamless handoff between AI systems and human support engineers. Analyze ticket, incident, and operational data to identify automation opportunities. Train, evaluate, and continuously improve ML models using real-world support data. Implement monitoring for model performance, drift, and accuracy in production. Ensure AI solutions meet reliability, security, and compliance standards. Implement guardrails, explainability, and auditability for AI-driven decisions. Contribute to AI governance and responsible AI practices.
Senior ML Operations (MLOps) Engineer
As a Senior ML Operations Engineer at Eight Sleep, you will pioneer cutting-edge ML technologies and integrate them into products and processes for health monitoring. You will own the design and operation of robust ML infrastructure by building scalable data, model, and deployment pipelines to ensure reliable model delivery to production. Your role involves partnering cross-functionally with R&D, firmware, data, and backend teams to ensure ML inference operates reliably and scales across Pods globally. You will optimize ML systems for cost-effectiveness, scalability, and high performance by managing compute, storage, and deployment resources during training and inference. Additionally, you will develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment, and maintain clear and direct communication within a remote work environment.
Tech Lead, Android Core Product - Buenos Aires, Argentina
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases; deploy and operate the core ML inference workloads for the AI Voices serving pipeline; introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models; build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Manual Quality Assurance Engineer, Web Core Product
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Mechanical Engineer with Python Experience - Freelance AI Trainer
Contributors may design graduate- and industry-level mechanical engineering problems grounded in real practice, evaluate AI-generated solutions for correctness, assumptions, and engineering logic, validate analytical or numerical results using Python (NumPy, SciPy, Pandas), improve AI reasoning to align with first principles and accepted engineering standards, and apply structured scoring criteria to assess multi-step problem solving.
Freelance Mechanical Engineering & Python Expert - AI Trainer
Contributors may design graduate- and industry-level mechanical engineering problems grounded in real practice, evaluate AI-generated solutions for correctness, assumptions, and engineering logic, validate analytical or numerical results using Python libraries such as NumPy, SciPy, and Pandas, improve AI reasoning to align with first principles and accepted engineering standards, and apply structured scoring criteria to assess multi-step problem solving.
Enterprise Account Executive (San Francisco)
Debug and fix issues in the Arize platform and ship pull requests with fixes. Build internal tools and copilots powered by generative AI to enhance the team. Rapidly prototype proof-of-concepts for customer use cases. Work across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.
Freelance Electrical Engineer with Python Experience - AI Trainer
Contributors may design rigorous electrical engineering problems reflecting professional practice, evaluate AI solutions for correctness, assumptions, and constraints, validate calculations or simulations using Python (NumPy, Pandas, SciPy), improve AI reasoning to align with industry-standard logic, and apply structured scoring criteria to multi-step problems.
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