Staff Machine Learning Engineer, Automation Anywhere

• 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

Desirable

• Automation Anywhere, the leader in Agentic Process Automation (APA), is seeking a Staff Machine Learning Engineer to help power the next generation of AI-driven digital agents transforming enterprise operations
• In this role, you will design, build, and deploy cutting-edge machine learning systems that operate at real-world scale—advancing Generative AI, Natural Language Processing, and Computer Vision capabilities within our industry-leading platform
• You will partner closely with product, engineering, data science, and platform teams to translate breakthrough research into high-impact production systems used by global enterprises
• This is a highly visible technical leadership opportunity where you will architect robust ML infrastructure, champion modern MLOps practices, and optimize performance, scalability, and reliability across distributed environments
• If you are passionate about turning advanced AI into enterprise-grade solutions that deliver measurable business outcomes, this is your chance to shape the future of intelligent automation at scale
• This role reports to our Director, ML Engineering
• 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