Machine Learning Operations Engineer
Optimize orchestration processes to ensure efficient deployment and management of AI models. Implement cost-saving strategies to minimize infrastructure expenses while maximizing performance. Upgrade throughput to enhance scalability and responsiveness of AI systems. Collaborate with cross-functional teams to identify bottlenecks and implement solutions to improve workflow efficiency. Ship new features and updates rapidly while maintaining high levels of quality and reliability. Deploy and monitor machine learning models produced by deep learning engineers. Design, deploy, and maintain performant and scalable processes for data acquisition and manipulation to enhance dataset accessibility. Participate actively in the team's software development process, including design reviews, code reviews, and brainstorming sessions. Maintain accurate and updated software development documentation.
Manager Information Security
You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This involves understanding the AI systems built, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.
Computer Vision Engineer
Conduct research on state-of-the-art Computer Vision methodologies and participate in the creation and curation of training and validation datasets. Perform statistical analyses and develop visualization tools to ensure data quality. Build and refine training pipelines and metrics to enhance model performance. Develop and optimize Computer Vision algorithms for multiple robotics/aerospace projects. Implement ML/CV models into production-ready environments, ensure seamless integration with Harmattan AI's systems, and conduct rigorous code reviews. Test algorithms in real-world environments and develop monitoring tools to track model performance and continuously improve deployed solutions. Work closely with software and simulation teams to align development with system requirements and communicate findings effectively to stakeholders.
Senior AI Engineer - USA
Senior AI Engineers are responsible for researching, building, optimizing, and deploying the production machine learning (ML) systems that thousands of developers integrate with their systems. Their work focuses on solving complex research and engineering problems to build the engine for the next generation of AI-driven software, particularly in the area of speech modeling including Speech-to-Text (STT) and Text-to-Speech (TTS).
Senior AI Engineer - United Kingdom
Senior AI Engineers at Inworld are responsible for researching, building, optimizing, and deploying production machine learning (ML) systems that thousands of developers integrate with their systems. Their work focuses on solving difficult research and engineering problems related to building the engine for the next generation of AI-driven software, with a primary focus on speech modeling including speech-to-text (STT) and text-to-speech (TTS). They address challenges unique to working with audio such as data collection, efficient training infrastructure, creating reinforcement learning alignment environments, and ultra-low latency inference optimizations.
Senior AI Engineer - Switzerland
Senior AI Engineers are responsible for researching, building, optimizing, and deploying the production machine learning systems that thousands of developers integrate with their systems. Their work focuses on solving difficult research and engineering problems related to building the engine for the next generation of AI-driven software, particularly in speech modeling (STT & TTS). This involves addressing challenges posed by audio data, such as data collection, efficient training infrastructure, creating reinforcement learning alignment environments, and ultra-low latency inference optimizations.
Senior AI Engineer - Canada
Senior AI Engineers at Inworld are responsible for researching, building, optimizing, and deploying production machine learning systems that support thousands of developers. Their work focuses on overcoming research and engineering challenges related to speech modeling, including speech-to-text and text-to-speech systems, addressing complex problems such as data collection, training infrastructure, reinforcement learning alignment environments, and ultra-low latency inference optimizations for AI-driven software.
Training: Process Management Engineer
As a Training Runtime: Process Management Engineer, you will design, build, and maintain software to orchestrate and monitor machine learning workloads on large supercomputers, working primarily with Python and Rust. Your responsibilities include profiling and optimizing the software stack to support computation orchestration at frontier scale, improving reliability, observability, and fault tolerance for long-running jobs, debugging complex distributed systems issues across large clusters, and responding to the changing shapes and needs of the ML systems to enable researchers. The role involves building high-performance asynchronous systems with a strong emphasis on performance, correctness, and scalability, and working on software that ties thousands of computers together as a unified system while promoting a fast debugging and development cycle and relentless optimization for scale, stability, and performance.
Software Engineer Systems Research Internship, Applied Emerging Talent (Summer 2026)
The responsibilities of the systems research internship include investigating hard systems problems at the intersection of systems engineering and research, building meaningful systems or prototypes, and carefully measuring their impact to improve Applied Systems' efficiency, scalability, and reliability. Typical focus areas are distributed systems and storage, compute and scheduling, performance engineering, reliability and observability, networking and data pipelines, and systems for machine learning. Internship projects may involve defining hypotheses, instrumenting existing production systems to gather metrics and analyze them, building or modifying real systems, conducting experiments and benchmarks, analyzing results, clearly communicating tradeoffs and recommendations, and publishing research in technical journals and conferences.
Senior Python Engineer - AI Testing Project (Freelance, Mindrift)
Create functional black box tests for large codebases in various source languages. Create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms. Monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards. Leverage LLMs such as Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.
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