PyTorch AI Jobs

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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).

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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.

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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.

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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.

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As a Forward Deployed ML Engineer at Modal, you will work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and optimize production AI workloads on Modal. You will contribute to open-source projects, publish technical content demonstrating Modal's capabilities across the AI stack, and collaborate with Modal's product and sales teams as both an engineer and a product stakeholder. Additionally, you will build trusted relationships with technical leaders at companies doing frontier AI work and conduct technical demos, experiments, and proof-of-concepts that highlight Modal's performance advantages.

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As a Senior AI Researcher for reinforcement learning, you will shape and improve the underlying RL methodology, maintain a high-quality training code-base, and conduct large-scale experiments to hill-climb performance benchmarks. You will conduct large-scale LLM training runs, analyze evaluation scores in depth, propose hypotheses for improvement, and directly implement them to maximize performance on benchmarks. You will identify, implement, and iterate on novel approaches to multi-turn reinforcement learning, optimize RL training loops for large-scale training by identifying bottlenecks, and collaborate cross-functionally to turn raw feedback into actionable training signals to ensure RL iterations lead to measurable improvements in downstream performance.

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Advance inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implement and maintain changes in high-performance inference engines including kernel backends, speculative decoding, and quantization. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Design and operate RL and post-training pipelines optimizing algorithms and systems where most cost is inference. Make RL and post-training workloads more efficient with inference-aware training loops, async RL rollouts, speculative decoding, and other techniques to reduce rollout collection and evaluation costs. Use these pipelines to train, evaluate, and iterate on frontier models. Co-design algorithms and infrastructure tightly coupling objectives, rollout collection, and evaluation to efficient inference, and identify bottlenecks across training engine, inference engine, data pipeline, and user-facing layers. Run experiments to understand trade-offs between model quality, latency, throughput, and cost, feeding insights back into design. Profile, debug, and optimize inference and post-training services under production workloads. Drive roadmap items requiring engine modifications such as kernel, memory layout, scheduling logic, and API changes. Establish metrics, benchmarks, and experimentation frameworks for rigorous validation of improvements. Provide technical leadership by setting technical direction for cross-team efforts in inference, RL, and post-training; mentor engineers and researchers on full-stack ML systems and performance engineering.

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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 (Roo Code, Claude) to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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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 large language models (LLMs) such as Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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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 (Roo Code, Claude) to accelerate development cycles, automate repetitive tasks, and improve overall code quality.