Staff Machine Learning Engineer

About Us:

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 industrys first Process Reasoning Engine (PRE) and specialized AI agents combines process discovery RPA end-to-end orchestration document processing and analyticsall 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 operations.

In this role you will design build and deploy cutting-edge machine learning systems that operate at real-world scaleadvancing 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 youll 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 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

The base salary range for this position is $155000 $175000 a year. The base salary ultimately offered is determined through a review of education industry experience training knowledge skills abilities of the applicant in alignment with market data and other factors. This position is also eligible for a discretionary bonus equity and a full range of medical and other benefits.

Ready to Revolutionize Work Join Us.

This is an opportunity to work with a global passionate team pioneering technology thats redefining the way people work everywhere. Join us and discover the many ways that you can have an impact achieve your potential and go be great.

Job Segment OR Key Words: SaaS Machine Learning ML Engineering NLP Generative AI APA Agentic Process Automation

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Benefits and perks youll appreciate:

• Flexible work schedule / remote roles
• Unlimited Personal Time Off
• 12 holidays off per year
• 4 days volunteer time off per year
• Eligible for 4 company Achievement days off per year
• Variety of health care and well-being benefits
• Paid family/parental leave
• We are a designated Best Place to Work for 2 years in a row! Learn morehere
• Newsweeks Top 100 Most Loved Workplaces in America 2023 Learn morehere