AI MLOps Engineer Jobs

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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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Collaborate directly with the GTM team including Account Executives and Solutions Architects to ensure smooth integration and successful deployment of machine learning solutions. Build and present compelling demonstrations and proof of concepts that showcase AI technology capabilities. Design, develop, and deploy end-to-end AI-powered applications tailored to customer needs. Contribute to the internal machine learning platform by adding features and fixing bugs. Integrate and enable new machine learning models into the existing platform or client environments. Improve system performance, efficiency, and scalability of deployed models and applications. Work closely with partners to enable joint AI solutions and ensure seamless collaboration.

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Design, build, and evaluate advanced workflows in self-hosted n8n environments. Architect multi-system integrations for scalable automation pipelines. Develop and optimize AI-powered workflows such as content generation, automation pipelines, and enrichment systems. Build and maintain lead generation, outreach, and data processing automation systems. Implement web scraping workflows and ensure reliable data extraction and processing. Optimize workflow execution, node sequencing, and error handling to prevent failures, delays, and API timeouts.

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As a Senior Machine Learning Engineer, responsibilities include leading technical scoping and architectural decisions for high-impact machine learning systems, designing and building production-grade ML software, tools, and scalable infrastructure, defining and implementing best practices and standards for deploying machine learning at scale across the business, collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities, acting as a trusted technical advisor to customers and partners by translating complex concepts into actionable strategies, and mentoring and developing junior engineers while actively shaping the team's engineering culture and technical depth.

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The responsibilities include advancing inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implementing and maintaining changes in high-performance inference engines, including kernel backends and speculative decoding, profiling and optimizing performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Unifying inference with RL/post-training by designing and operating RL and post-training pipelines, making RL and post-training workloads more efficient with inference-aware training loops, and using these pipelines to train, evaluate, and iterate on frontier models. Co-designing algorithms and infrastructure so that objectives, rollout collection, and evaluation are tightly coupled to efficient inference, identifying bottlenecks across the training engine, inference engine, data pipeline, and user-facing layers. Running ablations and scale-up experiments to understand trade-offs between model quality, latency, throughput, and cost, and feeding these insights back into model, RL, and system design. Owning critical systems at production scale by profiling, debugging, and optimizing inference and post-training services under real production workloads, driving roadmap items requiring engine modification, and establishing metrics, benchmarks, and experimentation frameworks to validate improvements rigorously. Providing technical leadership by setting technical direction for cross-team efforts at the intersection of inference, RL, and post-training, and mentoring other engineers and researchers on full-stack ML systems work and performance engineering.

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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 where most cost is inference, jointly optimizing algorithms and systems. Make RL and post-training workloads more efficient with inference-aware training loops, async RL rollouts, and speculative decoding. Use these 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, identifying bottlenecks across the training engine, inference engine, data pipeline, and user-facing layers. Run ablations and scale-up experiments to understand trade-offs between model quality, latency, throughput, and cost, feeding insights into model, RL, and system design. Profile, debug, and optimize inference and post-training services under real production workloads. Drive roadmap items requiring engine modification such as changing kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously. Provide technical leadership to set direction for cross-team efforts in inference, RL, and post-training and mentor engineers and researchers on full-stack ML systems work and performance engineering.

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Lead the team responsible for the AI/ML Stack infrastructure that bridges ML research and production, evolving the stack to meet large scale ML training and inference workload needs. Develop and execute a long-term vision and roadmap for the MLOps team to support ML development and deployment needs across business units, managing short-term deliveries and long-term architectural transformation. Lead and mentor a team of 6-7+ engineers, strategically allocate resources for support and strategic initiatives. Collaborate cross-functionally with leaders in machine learning, data science, product engineering, and infrastructure to identify pain points, address bottlenecks, and facilitate deployment of new solutions. Architect compute and storage pipelines to manage millions of slides and complex artifacts without data fragmentation or latency. Modernize AI product inference stack to support substantial growth in AI runs globally. Work with Site Reliability Engineering to establish comprehensive system observability metrics including compute utilization, network bottlenecks, and cost attribution. Conduct build versus buy assessments and lead stack refresh audits to benchmark proprietary tools against commercial and open-source alternatives.

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Build and maintain data pipelines for large video generation models, including data ingestion, parsing, filtering, preprocessing, and dataset curation at scale, using tools such as AWS S3 and DynamoDB. Design and run annotation workflows across platforms such as MTurk, Prolific, and Mechanical Turk, including task design, quality control, and label validation. Train, evaluate, and improve smaller supporting models used for data filtering, quality assessment, preprocessing, or other parts of the ML pipeline. Partner closely with research and engineering teams to turn experimental workflows into scalable, repeatable systems that support model training and evaluation. Own data quality across the pipeline by identifying bottlenecks, failure modes, and low-quality sources, and continuously improving tooling and processes. Build internal tools and automation that make it easier to prepare datasets, launch annotation jobs, monitor outputs, and support model development end to end. Drive larger pipeline projects from start to finish, such as new dataset creation efforts or upgrades to labeling and preprocessing infrastructure. Work within a Kubernetes-based training infrastructure, ensuring datasets are properly prepared, formatted, and delivered to training clusters. Profile and optimize research model inference scripts used in preprocessing steps, ensuring that model-driven filtering and transformation stages run within practical time and cost constraints when applied to large-scale raw data.

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Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference. Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies. Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques. Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation. Apply machine learning design patterns to build modular, reusable, and production-ready models. Collaborate with data engineers to develop high-performance data pipelines for training and inference. Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes. Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques.

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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 such as SGLang- or vLLM-style systems and Together's inference stack, including kernel backends, speculative decoding methods like ATLAS, and quantization. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Unify inference with RL/post-training by designing and operating RL and post-training pipelines where inference constitutes the majority of the cost, optimizing algorithms and systems jointly. Enhance RL and post-training workloads with inference-aware training loops, including asynchronous RL rollouts and speculative decoding techniques, making large-scale rollout collection and evaluation more efficient. Use these pipelines to train, evaluate, and iterate on cutting-edge models based on the inference stack. Co-design algorithms and infrastructure to tightly couple objectives, rollout collection, and evaluation to efficient inference, and identify bottlenecks across training engines, inference engines, data pipelines, and user-facing layers quickly. Run ablation and scale-up experiments to analyze trade-offs between model quality, latency, throughput, and cost, feeding insights back into model, RL, and system design. Own critical production-scale systems by profiling, debugging, and optimizing inference and post-training services under real production workloads. Lead roadmap initiatives necessitating engine modifications such as changes to kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks to rigorously validate improvements. Provide technical leadership by setting direction for cross-team efforts at the intersection of inference, RL, and post-training and mentor engineers and researchers on full-stack ML systems work and performance engineering.