Machine Learning Engineer

Position: Staff Machine Learning Engineer
Automation Anywhere is the leader in Agentic Process Automation (APA), transforming how work gets done with AI-powered automation. Its APA system, built on the industry’s first Process Reasoning Engine (PRE) and specialized AI agents, combines process discovery, RPA, end-to-end orchestration, document processing, and analytics—all delivered with enterprise-grade security and governance. Guided by its vision to fuel the future of work, Automation Anywhere helps organizations worldwide boost productivity, accelerate growth, and unleash human potential.
** Our opportunity:
** 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  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.
** Who you’ll report to:
** This role reports to our Director, ML Engineering
*
* Location:

** 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
** You will be a great fit if you have:
*** 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 Tensor Flow 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
*…