Applied ML Engineer Jobs

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As an ML Engineer focused on Text To Speech (TTS), you will own the deployment, optimization, and maintenance of production TTS systems. Responsibilities include deploying and optimizing large-scale TTS models into production environments for reliable, low-latency inference; implementing and refining post-training and modern inference techniques to maximize throughput and audio quality; collaborating with cross-functional teams to ensure seamless rollout, A/B testing, and iterative improvement of production models; maintaining high availability and scalable infrastructure for multi-speaker, expressive, and controllable TTS use cases; and designing and documenting best practices for efficient TTS inference and system reliability.

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As a Research Engineer in OpenAI's Applied Voice Team, you will design and build advanced machine learning models including state-of-the-art speech models such as speech-to-speech, transcribing, and text to speech, transforming research breakthroughs into tangible B2B applications like API and ChatGPT AVM. You will collaborate closely with software engineers, product managers, and forward deployed engineers to understand business challenges, address customer concerns, and deliver AI-powered solutions. You will implement scalable data pipelines, optimize models for performance and accuracy, ensure production readiness, and contribute to projects requiring cutting-edge technology and innovative approaches. Additionally, you will engage with the latest developments in machine learning and AI, participate in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices. You will also monitor and maintain deployed models to ensure they continue delivering value, thereby influencing how AI benefits individuals, businesses, and society.

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Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.

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Work alongside machine learning researchers, engineers, and product managers to bring our AI Voices to their customers for a diverse range of use cases. Deploy and operate the core ML inference workloads for our AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our deployed models. Build tools to give us visibility into our bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues.

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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 architectures to improve the performance, latency, throughput, and efficiency of deployed models. Build tools for visibility into bottlenecks and sources of instability and design and implement solutions to address high priority issues.

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Design, train, test, and iterate on diffusion models for 3D geological models. Design, train, test, and iterate on an approach for conditioning generation on geophysical data and other observations. Inform the generation of synthetic data to improve model performance. Adapt diffusion modeling approach to specific real-world projects in collaboration with project teams.

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You will develop ML/AI that leverage and extend the latest state-of-the-art methods and architectures, design experiments and conduct benchmarks to evaluate and improve their performance in real-world scenarios, collaborate with people across several teams and backgrounds to integrate cutting edge ML/AI in production systems, and work on AI-based capabilities and enabling infrastructure to allow semi-autonomous platforms to localise, navigate, and perceive the world in real time.

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Design original computational STEM problems that simulate real scientific workflows. Create problems that require Python programming to solve. Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains and creative problem-solving approaches. Verify solutions using Python with standard libraries (numpy, pandas, scipy, sklearn). Document problem statements clearly and provide verified correct answers.

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Design original computational STEM problems that simulate real scientific workflows. Create problems requiring Python programming to solve. Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes of days or weeks. Develop problems requiring non-trivial reasoning chains and creative problem-solving approaches. Verify solutions using Python with standard libraries such as numpy, pandas, scipy, and sklearn. Document problem statements clearly and provide verified correct answers.

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Design original computational STEM problems that simulate real scientific workflows. Create problems that require Python programming to solve. Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains and creative problem-solving approaches. Verify solutions using Python with standard libraries (numpy, pandas, scipy, sklearn). Document problem statements clearly and provide verified correct answers.