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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - **Company:** Automation Anywhere - **Location:** San Jose, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $155,000.0 - $175,000.0 - **Contract:** Permanent contract - **Skills:** Microsoft Excel, Artificial Intelligence, Amazon Web Services, Computer Vision, Big Data, Cloud Computing, Continuous Integration, Distributed Computing Environment, Apache Hadoop, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Azure Machine Learning, SQL Databases, Management of Software Versions, Cloud Platform System, Feature Engineering, Data Ingestion, Pytorch, System Availability, Large Language Models, Apache Spark, Generative AI, Containerization, AI Platforms, Kubernetes, ONNX (Open Neural Network Exchange) Format, Performance Monitor, Machine Learning Operations, Software Version Control, Docker - **Published:** May 28, 2026 - **Apply:** https://www.juju.com/job/00000000g3a87p ## About the Role + 7+ years of hands-on experience designing, building, and deploying machine learning models, with expertise in NLP, Computer Vision, and/or Generative AI solutions + Proven experience taking ML models from development to production, ensuring scalability, reliability, high availability, and ongoing performance monitoring + Strong proficiency in Python (required) and working knowledge of R and SQL, with experience leveraging big data technologies (e.g., Spark, Hadoop) for large-scale data processing and analytics + Deep experience with modern ML frameworks such as TensorFlow and PyTorch, including model training, evaluation, optimization (e.g., quantization, pruning), and inference performance tuning + Experience building and managing end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and lifecycle management + Hands-on experience implementing MLOps best practices, including CI/CD for ML, automated model versioning, monitoring for drift/performance, and workflow automation + Experience with cloud-based ML platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform) for training, deploying, and scaling models in cloud environments + Practical experience with containerization and orchestration tools (e.g., Docker, Kubernetes) and model serving platforms (e.g., Triton, ONNX) for production-grade deployments + Experience fine-tuning large language models (LLMs) and applying Generative AI techniques preferred + Familiarity with distributed training across multi-GPU or cloud environments preferred You excel in these key competencies: + Excellent problem-solving skills, with the ability to break down complex challenges in document extraction and transform them into scalable ML solutions + Strong communication skills, with the ability to articulate ML problems clearly and work autonomously + Ability to work cross-functionally with engineering, product, and data teams, influence technical direction without formal authority, and drive alignment across stakeholders in a fast-paced environment + Capacity to connect technical ML solutions to broader business objectives, prioritize high-impact initiatives, and make pragmatic trade-offs that balance innovation with production reliability + Demonstrates curiosity and agility in staying ahead of rapidly evolving AI/ML advancements, quickly evaluating new technologies, and applying them responsibly to real-world enterprise challenges ## Description Hybrid role with regular onsite work days in our San Jose, CA office strongly preferred. Other U.S locations may be considered. You will make an impact by being responsible for: + Developing and optimizing machine learning models leveraging NLP, Computer Vision, and GenAI + Architecting and implementing scalable ML pipelines for training, validation, deployment, and monitoring of production models + Driving the development of large-scale ML infrastructure, ensuring low-latency inference and efficient resource utilization across cloud and hybrid environments + Implementing MLOps best practices, automating model training, validation, deployment, and performance monitoring + Working closely with data engineers, software engineers, and product teams to ensure seamless integration of ML solutions into production systems + Optimizing ML models for performance, scalability, and efficiency, leveraging techniques like quantization, pruning, and distributed training + Enhancing model reliability by implementing automated monitoring, CI/CD pipelines, and versioning strategies + Leading efforts in data acquisition and preprocessing, including annotation and refinement of datasets to improve model accuracy + Staying updated with state-of-the-art ML research, identifying opportunities to integrate new techniques and technologies into production systems ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)