Machine Learning Engineer at ServiceNow

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

15 Dec, 25

Salary

0.0

Posted On

16 Sep, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, Large Language Models, Python, PyTorch, TensorFlow, HuggingFace Transformers, NumPy, Prompt Engineering, Model Training, Model Evaluation, Model Optimization, Data-Centric AI Workflows, MLOps, CI/CD, Docker, Kubernetes

Industry

Software Development

Description

Company Description It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone. Job Description Team Overview: Join our pioneering Core-LLM platform team, dedicated to pushing the boundaries of Generative AI. We focus on developing robust, scalable, and safe machine learning models, particularly LLMs, SLMs, Large Reasoning Models (LRMs) and SRMs that power cutting-edge ServiceNow products and features. As a Senior Manager, you will lead a talented team of machine learning engineers, shaping the future of our AI capabilities and ensuring the ethical and effective deployment of our technology. What you get to do in this role: Design, develop, and evaluate end-to-end machine learning solutions, with a focus on large language models (LLMs), combining engineering rigor and research depth. Lead the development of PoCs and applied research prototypes to explore novel AI capabilities, model interpretability, and safety strategies. Conduct cutting-edge experiments to assess model behaviour, generalization, and fairness across diverse datasets and use cases. Generate and curate synthetic and real-world datasets to optimize model robustness, reliability, and performance. Fine-tune and deploy large-scale models, incorporating prompt engineering, few-shot learning, and retrieval-augmented techniques. Collaborate cross-functionally with product, research, and engineering teams to publish white papers, participate in conferences, and contribute to open-source or peer-reviewed ML/AI research. Define and implement rigorous evaluation protocols, including human-in-the-loop testing, bias detection, and safety metrics. Develop CI/CD pipelines and containerized workflows for scalable training, evaluation, and deployment of ML solutions in production. Identify risks in AI applications and contribute to responsible AI initiatives, including transparency, robustness, and compliance frameworks. Qualifications Key qualifications: Experience in using AI Productivity tools such as Cursor, Windsurf, etc. is a plus or nice to have Experience with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization 2+ years of experience in machine learning, deep learning, LLM, with a track record of applied research or experimentation. Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, HuggingFace Transformers, and NumPy. Hands-on experience with prompt engineering, model training, evaluation, and optimization for LLMs or foundation models. Proven experience in applied research, academic publication, technical blogging, or contributions to open-source ML projects. Familiarity with data-centric AI workflows: synthetic data generation, labelling strategies, and dataset versioning tools. Deep understanding of AI/ML evaluation strategies, model robustness techniques, and responsible AI practices. Practical experience deploying models using inference platforms like Triton, ONNX in production environments. Experience working with MLOps stacks: CI/CD, experiment tracking (e.g., MLflow), Docker, Kubernetes, and distributed training frameworks. Excellent communication skills with the ability to explain complex ML ideas to non-technical stakeholders and contribute to scientific documentation. Additional Information Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact [email protected] for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2025 Fortune Media IP Limited. All rights reserved. Used under license.

Responsibilities

Design, develop, and evaluate end-to-end machine learning solutions, focusing on large language models. Lead the development of PoCs and applied research prototypes to explore novel AI capabilities and ensure ethical deployment.