Staff Machine Learning Engineer

Automation Anywhere•San Jose, CA

3d•$155,000 - $175,000•Hybrid

About The Position

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.

Requirements

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)
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

Nice To Haves

Experience fine-tuning large language models (LLMs) and applying Generative AI techniques preferred
Familiarity with distributed training across multi-GPU or cloud environments preferred

Responsibilities

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