TensorFlow AI Jobs

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As a Training: ML Framework Engineer, you will work on improving the training throughput for the internal training framework, enabling researchers to experiment with new ideas. Responsibilities include applying the latest techniques in the internal training framework to achieve hardware efficiency for training runs, profiling and optimizing the training framework, and working with researchers to enable the development of next-generation models.

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Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Explore the intersection of computer vision and robotic control to design systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation beyond simple grasping to sophisticated handling of diverse items. Collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities deployable on physical hardware for industrial applications. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms to enable robots to manipulate complex or deformable objects with high precision. Collaborate with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply advances in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.

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As an Engineering Manager on the Managed AI team, you will lead and scale a team of engineers building a next-generation platform for Large Language Models (LLMs). You will be responsible for guiding the team through designing and implementing highly scalable, fault-tolerant infrastructure. Your role includes leading, mentoring, and growing a team of software engineers; partnering with leadership to define and execute the AI roadmap; cultivating a high-performance, collaborative engineering culture; overseeing the architecture and development of core AI services such as fault-tolerant task queues, model management systems, and cost-aware scheduling; ensuring delivery of scalable systems capable of handling millions of API requests per second; delivering an AI platform to handle a large variety of load from training to agentic execution; working cross-functionally with Product, Infrastructure, and GTM stakeholders; representing Engineering in strategic discussions to influence AI platform growth and customer adoption; and promoting knowledge sharing, technical mentorship, and the evolution of engineering processes.

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The Senior Staff Engineer for the Model LifeCycle team is responsible for building a comprehensive managed platform for the entire application development lifecycle with a focus on leveraging Machine Learning models including Large Language Models (LLMs). Responsibilities include managing fine-tuning systems for large foundation models with multi-node orchestration, checkpointing, failure recovery, and cost-efficient scaling; implementing and maintaining end-to-end training pipelines for LLMs; developing distillation and reinforcement learning pipelines; managing agent execution infrastructure; and handling dataset, model, and experiment management such as versioning, lineage, evaluation, and reproducible fine-tuning at scale. The role also involves close collaboration with product, business, and platform teams to shape core abstractions and APIs, influencing long-term architectural decisions around training runtimes, scheduling, storage, and model lifecycle management. Additionally, the engineer will contribute to and engage with the open-source LLM ecosystem and take ownership of designing and building core systems from first principles.

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Lead the design and implementation of core AI services including resilient fault-tolerant queues for efficient task distribution, model catalogs for managing and versioning AI models, and scheduling mechanisms optimized for cost and performance. Architect and scale infrastructure to handle millions of API requests per second while ensuring robust monitoring and alerting for system health and 24/7 availability. Collaborate closely with product management, business strategy, and other engineering teams to define the AI platform roadmap, influence long-term vision and architectural decisions, contribute to open-source AI frameworks, participate in the AI community, and prototype and rapidly iterate on emerging technologies and new features.

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Conduct original research to advance the state of the art in machine learning and artificial intelligence. Design, implement, and evaluate novel algorithms, models, or training approaches at large scale. Collaborate with researchers and engineers to translate research insights into production systems and real-world applications.

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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, quantization, and improve performance across GPU, networking, and memory layers. Unify inference with RL/post-training by designing and operating RL and post-training pipelines that optimize inference costs, making these workloads more efficient through inference-aware training loops and related techniques. Use pipelines to train, evaluate, and iterate on frontier models. Co-design algorithms and infrastructure to tightly couple objectives, rollout collection, and evaluation with efficient inference and quickly 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, and use insights to inform model, RL, and system design. Own critical production-scale systems by profiling, debugging, and optimizing inference and post-training services under real workloads, driving roadmap items requiring engine modifications, and establishing metrics, benchmarks, and experimentation frameworks. Provide technical leadership by setting direction for cross-team efforts and mentoring engineers and researchers on full-stack ML systems and performance engineering.

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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 will involve understanding the AI systems we build, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and will collaborate with people across several teams and backgrounds.

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The role involves building a product that leverages AI to help teams analyze customer interviews, uncover insights, and make faster, smarter product decisions. Responsibilities include solving problems end-to-end across the LLM pipeline, infrastructure, backend, and UX; making architectural decisions for a greenfield stack; pushing the capabilities of large language models (LLMs); and communicating clearly about tradeoffs, problems, and blockers. The position requires ownership of parts of the product, ensuring high quality output, and working on advanced AI models to deliver nuanced and effective research tools such as AI agents for project setup, interview conduction, analysis of responses, database building of people profiles, real-time video interview systems with emotional understanding, distributed information mining, and models of customer preferences and synthetic personas.

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As a Member of Technical Staff, Design at Listen Labs, you will be responsible for building a product that changes how companies make decisions by tackling complex problems end-to-end. You will own a part of the product and make decisions across the LLM pipeline, infrastructure, backend, and UX. The role involves pushing the most advanced AI models to their limits and working with foundational companies on new AI model releases. You will help build and enhance key technical challenges including developing a research AI agent, constructing a comprehensive database of human profiles, enabling realtime video interviews with emotional understanding, creating an agent for distributed information mining, and modeling customer preferences and synthetic personas. The position requires caring deeply about the product quality, obsessing about details, having strong product instincts, and being able to communicate tradeoffs, problems, and blockers directly with minimal meetings.