Staff Machine Learning Engineer, Automation Anywhere
$190-205k
+ Discretionary Bonus + Equity
Office located in San Jose, CA
Enterprise AI agents for business processes
Be an early applicant
Enterprise AI agents for business processes
1001+ employees
Be an early applicant
$190-205k
+ Discretionary Bonus + Equity
Office located in San Jose, CA
1001+ employees
Company mission
To democratize automation and create a better future for everyone, liberating people from mundane, repetitive tasks, and allowing them more time to use their intellect and creativity.
Role
Who you are
- Bachelor’s or Master’s degree in Computer Science, Data Science, or related field; advanced degrees are a plus
- 7+ years of hands-on experience developing and deploying ML models, particularly in NLP, Computer Vision, or Generative AI. Proven success in production deployments with a focus on scalability, reliability, and availability
- Proficient in Python, R, SQL, and working with big data technologies (e.g., Spark, Hadoop)
- Skilled in modern ML frameworks like TensorFlow and PyTorch
- Experience building ML pipelines and implementing MLOps practices to automate and scale workflows
- Familiar with cloud-based ML services (e.g., AWS SageMaker, Azure ML, Google AI Platform)
- Hands-on with containerization (Docker), orchestration (Kubernetes), and model serving platforms (e.g., Triton Inference Server, ONNX)
- Experience fine-tuning large language models and applying Generative AI techniques
- Familiarity with model optimization methods (e.g., quantization, pruning) for deployment on cloud or edge devices
- Exposure to distributed training across GPUs or cloud environments, CI/CD pipelines for ML, automated model versioning, and performance monitoring tools — all considered a plus
- Strong understanding of the complete ML lifecycle including data collection, feature engineering, model training, evaluation, and deployment
- Strategic problem-solving — You approach complex ML challenges with creativity and clarity, especially in areas like document extraction, classification, and language understanding, designing solutions that are both effective and scalable
- Clear and collaborative communication — You explain technical concepts with ease to cross-functional teams and stakeholders, and you thrive in both autonomous and collaborative work environments
- Product-focused mindset — You build models not just for accuracy, but for real-world performance, reliability, and user impact—always thinking about how your work drives value in production
- Adaptability and continuous learning — You stay current with the latest ML advancements, proactively seeking opportunities to apply new techniques and tools that improve speed, accuracy, and efficiency
- Bias for action and ownership — You take initiative, move ideas forward quickly, and are comfortable leading through ambiguity to deliver high-quality, production-ready ML solutions
What the job involves
- Automation Anywhere, the leader in Agentic Process Automation (APA), is looking for a Staff Machine Learning Engineer to help shape the future of intelligent automation
- In this role, you’ll design, build, and deploy cutting-edge ML models focused on Generative AI, NLP, and Computer Vision, powering digital agents that learn, adapt, and deliver real-world impact
- You’ll drive scalable ML infrastructure, implement robust MLOps practices, and collaborate closely with cross-functional teams to seamlessly integrate AI into our core products
- If you're passionate about building innovative AI systems that operate at scale and excited to be part of a team pushing the boundaries of what’s possible in automation—we’d love to hear from you
- This role reports to our Director, ML Engineering
- Developing and optimizing machine learning models using NLP, Computer Vision, and Generative AI to address complex business challenges
- Architecting and scaling end-to-end ML pipelines for model training, validation, deployment, and monitoring in production environments
- Driving the development of high-performance ML infrastructure to support low-latency inference and efficient resource use across cloud and hybrid systems
- Implementing MLOps best practices to automate model workflows, including training, testing, deployment, monitoring, and versioning
- Collaborating closely with data engineers, software developers, and product managers to ensure seamless integration of ML models into production systems
- Optimizing models for performance, accuracy, and scalability using techniques such as quantization, pruning, and distributed training
- Improving system reliability by building CI/CD pipelines, automating monitoring, and managing model versions throughout the ML lifecycle
- Leading data initiatives for acquiring, annotating, and preprocessing datasets to enhance model quality and accuracy
- Staying current with the latest ML research and trends, and identifying opportunities to incorporate new technologies into production workflows
Our take
Although technology is supposed to save labor, often it doesn't feel like it, with the average desk worker stuck carrying out an increasing number of repetitive manual tasks just to make their growing software stack function. Enter Automation Anywhere, a provider of robotic process automation (RPA) software designed to handle tedious repetitive functions, freeing teams for more productive work.
RPA is a highly competitive field, with firms like Celonis attracting vast amounts of funding and Microsoft beginning to bundle RPA functionality with its business packages. What helps Automation Anywhere stand out is its use of AI to discover the processes that can be automated within a company, a task that is usually carried out by expensive consultancy firms. This has allowed Automation Anywhere to grow a huge client base, including the likes of the NHS, Dell and Staley Black & Decker.
Fueled by partnerships with Microsoft, AWS, and PwC India, Automation Anywhere is accelerating its leadership in AI-powered automation. These collaborations allow the company to deliver cutting-edge solutions and aid in its continued growth.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
13% employee growth in 12 months
Company
Company HQ
North San Jose, San Jose, CA
Leadership
Mihir Shukla
(Co-Founder & CEO)Previously the Director of Engineering at E2Open and Infoseek, and a Consultant at Netscape.
Neeti Mehta Shukla
(Co-Founder & Social Impact Officer)Previously a Marketing Research Consultant for Intuit. They are also a Limited Partner at Kalaari Capital and CXXO Champion for CXXO.
Rushabh Parmani
(Co-Founder & Chief Customer Officer)Before co-founding Automation Anywhere they were a Software Engineer at Siebel Systems.
Ankur Kothari
(Co-Founder & COO)Previously a member of the Core Group in Siebel Systems. Since co-founding Automation Anywhere, they have been a CRO, COO, CSO and Chief Customer & Strategy Officer before becoming COO in 2023.
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