Applied Scientist 2 at Microsoft

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

03 Mar, 26

Salary

0.0

Posted On

03 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Artificial Intelligence, Deep Learning, Generative AI, Reinforcement Learning, Model Deployment, Python, TensorFlow, PyTorch, Scikit-Learn, Data Preprocessing, Feature Engineering, Statistical Proficiency, Cross-Functional Collaboration, Communication Skills, Responsible AI

Industry

Software Development

Description

1. ML & AI Development Develop novel algorithms and architectures, optimizing real-world deployment constraints such as latency, efficiency, and scalability. Leverage cutting-edge advancements in deep learning, generative AI, reinforcement learning, and large-scale ML systems to push the boundaries of AI innovation. Build and deploy ML/AI models at scale, ensuring seamless integration into production systems with minimal latency and maximum efficiency. Develop robust pipelines for data preprocessing, feature engineering, model training, and deployment, ensuring high-quality input data and reproducibility. Ensure efficient retraining and model versioning, enabling rapid experimentation and continuous learning in production environments. Ensure all ML models adhere to security, privacy, and ethical AI standards, including fairness, explainability, and regulatory compliance. Implement techniques for bias detection, adversarial robustness, and secure AI deployment to mitigate risks in real-world applications. Work closely with data science, engineering, and product teams to align AI initiatives with business objectives and technical feasibility. Communicate complex ML concepts and results to senior leadership, product teams, and stakeholders, ensuring alignment on AI strategies and outcomes. Stay at the forefront of AI and ML research, actively exploring new algorithms, architectures, and applications in deep learning, NLP, CV, and more. Contribute to AI research communities, publish papers, attend conferences, and engage in collaborations with academia and industry partners. B.Sc in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, or a related field. Industry Experience: 3+ years of hands-on experience in designing, developing, and deploying machine learning models at scale in production environments. ML & AI Expertise: Strong theoretical and practical knowledge of supervised and unsupervised learning, deep learning, generative AI, reinforcement learning, probabilistic modeling, and large-scale ML systems. Programming & Development Skills: Proficiency in Python with deep expertise in ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-Learn, Hugging Face, or similar. Model Deployment Experience: Experience in deploying and optimizing ML models in cloud-based environments (Azure, AWS, GCP). Data Handling & Feature Engineering: Expertise in working with large-scale datasets, time-series data, structured/unstructured data, and applying advanced feature engineering techniques. Mathematical & Statistical Proficiency: Strong foundation in linear algebra, probability, optimization, Bayesian inference, and numerical methods. Cross-Functional Collaboration: Ability to work closely with software engineers, product managers, and business stakeholders to translate business needs into AI solutions. Communication Skills: Ability to clearly articulate complex ML concepts, write technical reports, and present findings to both technical and non-technical audiences. Ph.D. in ML/AI or Related Field: Strong research background with contributions to top-tier ML/AI conferences (NeurIPS, ICML, CVPR, ACL, etc.). Experience with Large-Scale AI Systems: Experience working with LLMs, foundation models, multimodal learning, transformers, and generative AI for real-world applications. Responsible AI & Compliance: Familiarity with fairness, interpretability, privacy-preserving AI (e.g., differential privacy, federated learning), and AI governance frameworks. End-to-End AI Product Development: Experience integrating ML models into real-time applications, APIs, or enterprise software solutions.

Responsibilities

The Applied Scientist 2 will develop novel algorithms and architectures for ML & AI, ensuring efficient deployment and integration into production systems. They will work closely with cross-functional teams to align AI initiatives with business objectives and stay updated on advancements in AI research.