ml engineer

Amadeus

<div>
 
 Job Title
 
 
 
 Data Scientist 3
 
 
 
 About The Role
 
 
 
 You will join a multidisciplinary team of software engineers, data engineers, data scientists, product specialists, and designers working on next-generation
 
 AI-powered solutions
 
 for airport and airline operations. As part of the Data Spark
 
 Agile Release Train
 
 , you will collaborate closely with colleagues in Nice and London.
 
 
 Your key responsibility will be to
 
 design, automate, and optimize machine learning workflows
 
 that power:
 
 
 <ul>
 <li>
 Real-time analytics and inferencing
 </li>
 <li>
 Predictive modeling
 </li>
 <li>
 ML foundations for generative AI capabilities
 
 
 </li>
 </ul>
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Lead the design, implementation, and maintenance of scalable, cloud-native ML pipelines (primarily on Microsoft Azure within a multi-cloud strategy) for batch and streaming workloads.
 </li>
 <li>
 Automate the end-to-end lifecycle of ML models: data preparation, experimentation, tuning, deployment, monitoring, and continuous improvement with a focus on reproducibility and governance.
 </li>
 <li>
 Collaborate with software engineers to ensure robust integration of ML models into production systems, including API contracts, SLAs, fallbacks, and graceful degradation strategies.
 
 
 </li>
 </ul>
 
 About The Ideal Candidate
 
 
 
 <ul>
 <li>
 Proven experience building and maintaining production-grade ML pipelines in cloud environments across real-world projects.
 </li>
 <li>
 Strong hands-on experience with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) and solid understanding of model development patterns (supervised learning, time-series forecasting, model evaluation).
 </li>
 <li>
 Practical MLOps expertise with tools like Azure Machine Learning, MLflow, or similar platforms for experiment tracking, pipeline orchestration, and model deployment.
 </li>
 <li>
 Strong command of Python for production-grade deliveries, including testing, code reviews, documentation, packaging, and version control.
 </li>
 <li>
 Experience working in Agile teams (Scrum, SAFe).
 
 
 </li>
 </ul>
 
 
 
 Diversity &amp; Inclusion
 
 
 
 
 
 Amadeus aspires to be a leader in Diversity, Equity and Inclusion in the tech industry, enabling every employee to reach their full potential by fostering a culture of belonging and fair treatment, attracting the best talent from all backgrounds, and as a role model for an inclusive employee experience.
 
 
 Amadeus is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to gender, race, ethnicity, sexual orientation, age, beliefs, disability or any other characteristics protected by law.
</div>

Data Scientist 3

Myntra

<div>
 
 About The Company
 
 
 
 
 Who are we?
 
 
 
 Myntra is India&rsquo;s leading fashion and lifestyle platform, where technology meets creativity. As pioneers in fashion e-commerce, we&rsquo;ve always believed in disrupting the ordinary.
 
 
 We thrive on a shared passion for fashion, a drive to innovate to lead, and an environment that empowers each one of us to pave our own way. We&rsquo;re bold in our thinking, agile in our execution, and collaborative in spirit.
 
 
 Here, we create MAGIC by inspiring vibrant and joyous self-expression and expanding fashion possibilities for India, while staying true to what we believe in.
 
 
 We believe in taking bold bets and changing the fashion landscape of India. We are a company that is constantly evolving into newer and better forms and we look for people who are ready to evolve with us.
 
 
 From our humble beginnings as a customization company in 2007 to being technology and fashion pioneers today, Myntra is going places and we want you to take part in this journey with us.
 
 
 Working at Myntra is challenging but fun - we are a young and dynamic team, firm believers in meritocracy, believe in equal opportunity, encourage intellectual curiosity and empower our teams with the right tools, space, and opportunities.
 
 
 
 About Team
 
 
 
 The Myntra Data Science team is at the forefront of innovation, delivering cutting-edge solutions that drive significant revenue and enhance customer experiences across various touchpoints. Every quarter, our models impact millions of customers, leveraging real-time, near-real-time, and offline solutions with diverse latency requirements. These models are built on massive datasets, allowing for deep learning and growth opportunities within a rapidly expanding organization. By joining our team, you'll gain hands-on experience with an extensive e-commerce platform, learning to develop models that handle millions of requests per second with sub-second latency.
 
 
 We take pride in deploying solutions that not only utilize state-of-the-art machine learning techniques&mdash;such as graph neural networks, diffusion models, transformers, representation learning, optimization methods, and Bayesian modeling&mdash;but also contribute to the research community with multiple peer-reviewed publications.
 
 
 Roles and Responsibilities
 
 
 Design, Develop, and Deploy: Create and implement advanced machine learning models and algorithms to tackle complex business challenges across various domains, including Recommender Systems, Search, Computer Vision, Supply Chain Management (SCM), Pricing, Forecasting, Trend and Virality Prediction, Generative AI, and more.
 
 
 Technical Expertise: Demonstrate deep theoretical knowledge and practical expertise in one or more areas, such as Natural Language Processing (NLP), Computer Vision, Recommender Systems, and Optimization.
 
 
 Software Development: Develop robust, scalable, and maintainable software solutions for seamless model deployment.
 
 
 CI/CD Pipelines: Set up and manage Continuous Integration/Continuous Deployment (CI/CD) pipelines for automated testing, deployment, and model integration.
 
 
 Model Maintenance: Maintain and optimize machine learning pipelines, including data cleaning, feature extraction, and model training.
 
 
 Collaboration: Work closely with the Platforms and Engineering teams to ensure smooth deployment and integration of ML models into production systems.
 
 
 Data Management: Partner with the Data Platforms team to gather, process, and analyze data crucial for model development.
 
 
 Code Quality: Write clean, efficient, and maintainable code following best practices.
 
 
 Performance Optimization: Conduct performance testing, troubleshooting, and tuning to ensure optimal model performance.
 
 
 Continuous Learning: Stay up-to-date with the latest advancements in machine learning and technology, sharing insights and knowledge across the organization.
 
 
 
 Qualifications &amp; Experience
 
 
 
 <ul>
 <li>
 Industry Experience: 5+ years with a Bachelor's degree, or 3+ years with a Master&rsquo;s/Ph.D. in Computer Science, Mathematics, Statistics, or a related field.
 </li>
 <li>
 Machine Learning Expertise: At least 2 years of hands-on experience as a Machine Learning Engineer or a similar role.
 </li>
 <li>
 Production Deployment: Proven experience deploying machine learning solutions into production environments.
 </li>
 <li>
 Programming Skills: Strong proficiency in Python or equivalent programming languages for model development.
 </li>
 <li>
 ML Frameworks: Familiarity with leading machine learning frameworks (Keras, TensorFlow, PyTorch) and libraries (scikit-learn).
 </li>
 <li>
 CI/CD Tools: Experience with CI/CD tools and practices.
 </li>
 <li>
 Communication: Excellent verbal and written communication skills.
 </li>
 <li>
 Teamwork &amp; Independence: Ability to work collaboratively in a team environment or independently as needed.
 </li>
 <li>
 Workload Management: Strong organizational skills to manage and prioritize tasks, supporting your manager effectively.
 
 
 </li>
 </ul>
 Exceptional candidates are encouraged to apply, even if you don't meet every listed qualification. We're open to hiring individuals who demonstrate outstanding potential.
 
 
 Nice to Have
 
 
 <ul>
 <li>
 Research Contributions: Publications or presentations in recognized Machine Learning and Data Science journals/conferences.
 </li>
 <li>
 Big Data Technologies: Experience with big data technologies like Spark or other distributed computing frameworks.
 </li>
 <li>
 Cloud Services: Proficiency in cloud platforms (AWS, Google Cloud) and an understanding of distributed systems.
 </li>
 <li>
 Generative AI Exposure: Familiarity with Generative AI models.
 </li>
 <li>
 Database Management: Experience with SQL and/or NoSQL databases.
 </li>
 <li>
 ML Orchestration: Knowledge of ML orchestration tools (Airflow, Kubeflow, MLFlow).
 
 
 </li>
 </ul>
 
 Required Skills
 
 
 
 python, machine learning, recommendation system, forecasting, deployment, time series
 
 
 
 Required Education
 
 
 
 B.Tech
</div>

Senior Data Scientist

Amazon

<div>
 
 Description
 
 
 
 Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon&rsquo;s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and team for India Consumer Businesses.
 
 
 If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you.
 
 
 Major responsibilities
 
 
 <ul>
 <li>
 Use machine learning and analytical techniques to create scalable solutions for business problems
 </li>
 <li>
 Analyze and extract relevant information from large amounts of Amazon&rsquo;s historical business data to help automate and optimize key processes
 </li>
 <li>
 Design, development, evaluate and deploy innovative and highly scalable models for predictive learning
 </li>
 <li>
 Research and implement novel machine learning and statistical approaches
 </li>
 <li>
 Work closely with software engineering teams to drive real-time model implementations and new feature creations
 </li>
 <li>
 Work closely with business owners and operations staff to optimize various business operations
 </li>
 <li>
 Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
 </li>
 <li>
 Mentor other scientists and engineers in the use of ML techniques
 
 
 </li>
 </ul>
 
 About The Team
 
 
 
 The India Machine Learning team works closely with the business and engineering teams in building ML solutions that create an impact for Amazon's IN businesses. This is a great opportunity to leverage your machine learning and data mining skills to create a direct impact on consumers and end users.
 
 
 
 Basic Qualifications
 
 
 
 <ul>
 <li>
 Master's degree or above in computer science, computer engineering, or related field
 </li>
 <li>
 Experience programming in Java, C++, Python or related language
 </li>
 <li>
 3+ years of building models for business application experience
 </li>
 <li>
 PhD, or a Master's degree and experience building machine learning models or developing algorithms for business application
 </li>
 <li>
 Experience in patents or publications at top-tier peer-reviewed conferences or journals
 </li>
 <li>
 Experience programming in Java, C++, Python or related language
 </li>
 <li>
 Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience implementing algorithms using both toolkits and self-developed code
 </li>
 <li>
 Have publications at top-tier peer-reviewed conferences or journals
 
 
 </li>
 </ul>
 Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner.
 
 
 
 
 Company
 
 - ADCI - Karnataka
 
 
 Job ID: A3089323
</div>

Applied Scientist, India Machine Learning

Glance

<div>
 
 
 What you will be doing
 
 
 
 We are looking for a Data Scientist who can operate at the intersection of
 
 classical machine learning
 
 ,
 
 large-scale recommendation systems
 
 , and
 
 modern agentic AI systems
 
 .
 
 
 You will design, build, and deploy intelligent systems that power Glance&rsquo;s personalized lock screen and live entertainment experiences. This role blends deep ML craftsmanship with forward-looking innovation in autonomous/agentic systems.
 
 
 
 Your responsibilities will include:
 
 
 
 
 Classical ML &amp; Recommendation Systems
 
 
 <ul>
 <li>
 Design and develop large-scale recommendation systems using advanced ML, statistical modelling, ranking algorithms, and deep learning.
 </li>
 <li>
 Build and operate machine learning models on diverse, high-volume data sources for personalization, prediction, and content understanding.
 </li>
 <li>
 Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
 </li>
 <li>
 Own data preparation, model training, evaluation, and deployment pipelines in collaboration with engineering counterparts.
 </li>
 <li>
 Monitor ML model performance using statistical techniques; identify drifts, failure modes, and improvement opportunities.
 </li>
 </ul>
 
 
 Agentic Systems &amp; Next-Gen AI
 
 
 <ul>
 <li>
 Build and experiment with
 
 agentic AI systems
 
 that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention.
 </li>
 <li>
 Apply
 
 LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models
 
 for semantic understanding, content classification, and user preference modelling.
 </li>
 <li>
 Design intelligent agents that can automate repetitive decision-making tasks&mdash;e.g., candidate generation tuning, feature selection, or context-aware content curation.
 </li>
 <li>
 Explore reinforcement learning, contextual bandits, and self-improving systems to power next-generation personalization.
 </li>
 </ul>
 
 
 Cross-functional impact
 
 
 <ul>
 <li>
 Collaborate with Designers, UX Researchers, Product Managers, and Software Engineers to integrate ML and GenAI-driven features into Glance&rsquo;s consumer experiences.
 </li>
 <li>
 Contribute to Glance&rsquo;s ML/AI thought leadership&mdash;blogs, case studies, internal tech talks, and industry conferences.
 </li>
 <li>
 Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams.
 </li>
 <li>
 Plus: Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities.
 </li>
 </ul>
 
 
 
 
 We are seeking candidates with deep expertise in ML, recommendation systems, and a strong appetite for building agentic AI systems.
 
 
 You should have experience with:
 
 <ul>
 <li>
 Large-scale ML and recommendation systems (collaborative filtering, ranking models, content-based approaches, embeddings).
 </li>
 <li>
 Classical ML and deep learning techniques across NLP, sequence modelling, RL, clustering, and time series.
 </li>
 <li>
 Experience in deploying ML workflows/models in production system
 </li>
 <li>
 Big data processing (Spark, distributed data systems) and cloud computing.
 </li>
 <li>
 Designing end-to-end ML solutions&mdash;from prototype to production.
 </li>
 <li>
 Plus: Building or experimenting with LLMs, generative models, and agentic AI workflows (e.g., autonomous evaluators, self-improving pipelines, automated experiment agents).
 </li>
 </ul>
 
 
 
 
 
 Qualifications
 
 
 <ul>
 <li>
 Bachelor&rsquo;s/master&rsquo;s in computer science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a plus.
 </li>
 <li>
 6+ years of industry experience in ML/Data Science, ideally in large-scale recommendation systems or personalization.
 </li>
 <li>
 Experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable.
 </li>
 <li>
 Expertise with algorithms in NLP, Reinforcement Learning, Time Series, and Deep Learning, applied on real-world datasets.
 </li>
 <li>
 Proficient in Python and comfortable with statistical tools (R, NumPy, SciPy, PyTorch/TensorFlow, etc.).
 </li>
 <li>
 Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI).
 </li>
 <li>
 Comfortable working in cross-functional teams.
 </li>
 <li>
 Familiarity with privacy-preserving ML and identity-less ecosystems (especially on iOS and Android).
 </li>
 <li>
 Excellent communication skills with the ability to simplify complex technical concepts.
 </li>
 </ul>
 
 
 
 We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impact.
 
 
 Our team includes engineers, physicists, economists, mathematicians, and social scientists&mdash;a great data scientist can come from anywhere.
 
</div>

Machine Learning Engineer III - Recommendation Systems

Uber

<div>
 
 About The Role
 
 
 
 The Security Engineering team at Uber is focused on making the production and corporate environments secure by default to provide industry-leading solutions for Uber's production services and infrastructure. We are looking for a Software Engineer to join our Enterprise Security team. This team is dedicated to protecting Uber's digital assets by designing and deploying secure architectures across enterprise applications. In this role, you'll leverage your software engineering skills to develop systems that enhance the security posture and elevate security standards. This is a unique opportunity to collaborate with both seasoned and emerging talent, make a tangible impact on Uber's security, and further refine your technical and security expertise.
 
 
 ---- What the Candidate Will Do ----
 
 
 <ul>
 <li>
 Design features and systems that solve problems related to enterprise application security
 </li>
 <li>
 Write clean, modular, and maintainable code.
 </li>
 <li>
 Deploy, configure, and manage a variety of enterprise security solutions, and the Enterprise Application Security Posture Management (SSPM) platform.
 
 
 </li>
 </ul>
 
 Basic Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's or Master's degree in Computer Science, Engineering, or related field.
 </li>
 <li>
 3+ years of experience in software engineering
 </li>
 <li>
 Strong hands-on technical skills, including proficiency in one or more programming languages (Go, Java, Python, C/C++).
 </li>
 <li>
 Strong problem solving, service architecture, coding and code reviews skills
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience with integrating with SaaS applications
 </li>
 <li>
 Experience with CyberSecurity
 </li>
 <li>
 Experience building out integrations with open-source and vendor products
 </li>
 <li>
 Experience with AI/ML technologies / frameworks and incorporating them into production systems
 </li>
 <li>
 Experience with automation and scripting (e.g., Python, Bash) for security operations
 </li>
 </ul>
</div>

Software Engineer II - Backend

Allucent

<div>
 
 The Senior / Principal Data Engineer will design, develop, and maintain scalable data platform and analytics solutions across data lakes and operational databases. This role requires hands-on expertise in Azure Databricks, Azure SQL, Python/PySpark, Notebooks, with a strong understanding of data modeling, ETL/ELT best practices, and CI/CD automation in Azure DevOps. The ideal candidate will have a proven record of building robust, efficient, and secure data pipelines that enable analytics, reporting, and AI/ML solutions, preferably in life sciences, clinical research, or healthcare domains.&nbsp;
 
 
 
 
 Requirements
 
 
 
 
 
 Data Architecture &amp; Engineering&nbsp;
 
 <ul>
 <li>
 Design and implement end-to-end data pipelines using Azure Databricks, Azure Data Factory, and ADLS Gen2.&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Build scalable and performant data models for data lakes (Medallion architecture), data warehouses, and operational systems.&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Develop ELT/ETL frameworks for ingestion from APIs, relational sources, flat files, and third-party systems (e.g., Dynamics 365, Veeva, EDC).&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Optimize data transformations, partitioning, and delta lake performance for analytics workloads.&nbsp;
 </li>
 </ul>
 
 Data Integration &amp; Automation&nbsp;&nbsp;
 
 <ul>
 <li>
 Leverage Python and PySpark for data ingestion, cleansing, enrichment, and advanced transformations.&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Implement CI/CD pipelines for data workflows using Azure DevOps and Git, including automated testing, deployment, and monitoring.&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Develop and integrate RESTful APIs for cross-system data exchange and automation.&nbsp;
 </li>
 </ul>
 
 Analytics Enablement&nbsp;
 
 <ul>
 <li>
 Collaborate with the BI team to ensure clean, high-quality, and accessible data for the Power BI platform.&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Support semantic modeling, metric layer design, and data governance best practices.&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Enable advanced analytics by provisioning data for ML/AI initiatives and predictive insights.&nbsp;
 </li>
 </ul>
 
 Cross Functional Collaboration&nbsp;
 
 <ul>
 <li>
 Collaborate with product/system owners, analysts, and business stakeholders to translate analytical requirements into technical data solutions.&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Drive best practices in Agile development, version control, and DevOps workflows.&nbsp;
 </li>
 </ul>
 
 &nbsp;
 
 
 Education Requirements and Qualifications&nbsp;
 
 
 
 Qualifications&nbsp;&nbsp;
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Information Systems, Engineering, or related field (Master's preferred).&nbsp;&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Minimum 5-8 years of relevant experience building and maintaining data solutions (data lakes, data warehouses, operational databases).&nbsp;&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Expert-level proficiency in Azure Databricks, PySpark, SQL, and Azure DevOps.&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Proven experience with Azure Data Factory, ADLS Gen2, and Azure SQL Server.&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Working knowledge of CI/CD automation, version control (Git), and infrastructure as code (ARM or Terraform).&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Experience with Power BI or similar analytics platforms (Tableau, Looker) required; experience with Snowflake, Redshift, or Synapse Analytics is a plus.&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Strong analytical, debugging, and performance-tuning skills.&nbsp;
 </li>
 </ul>
 <ul>
 <li>
 Experience in life sciences or healthcare industries is a strong plus.&nbsp;
 </li>
 </ul>
 
 Skills&nbsp;
 
 
 Core expertise: Expert-level in Databricks, PySpark, SQL, and Azure DevOps&nbsp;
 
 
 Data engineering: Data modeling, Delta Lake optimization, ETL/ELT design, distributed processing.&nbsp;
 
 
 Integration &amp; Automation: Azure Data Factory, REST APIs, CI/CD pipelines, Git branching strategies.&nbsp;
 
 
 Analytics &amp; BI: Power BI (Tableau), semantic layer design, DAX/SQL tuning.&nbsp;
 
 
 Cloud &amp; DevOps: Azure ecosystem (ADF, ADLS, Azure SQL, Synapse), Infrastructure as Code&nbsp;
 
 
 Data Governance &amp; Quality: Metadata management, data validation frameworks, logging and monitoring.&nbsp;
 
 
 Soft skills: Good communication, mentoring, Agile teamwork, analytical thinking, collaboration.&nbsp;
 
 
 &nbsp;
 
</div>

Data Developer

Scouto AI

<div>
 Position Summary
 
 
 As a Computational Materials Discovery Scientist, you will work at the intersection of materials science, computational chemistry, condensed matter physics, quantum computing, and AI/ML. You will contribute to first-principles simulations, molecular and mesoscale modeling, materials informatics pipelines, and hybrid quantum&ndash;classical algorithms for accelerated discovery of materials, catalysts, semiconductors, polymers, and functional systems.
 
 
 This role is ideal for candidates who want to solve real scientific and industrial problems using DFT, MD, multiscale modeling, machine learning, and emerging quantum computing techniques, fully integrated with QpiAI&rsquo;s AI and quantum computing platforms.
 
 
 
 Requirements
 
 
 
 Core Technical Skills
 
 
 <ul>
 <li>
 Computational &amp; Quantum Mechanical Methods
 
 
 </li>
 </ul>
 Electronic Structure &amp; Quantum Methods
 
 
 <ul>
 <li>
 Density Functional Theory (DFT)
 </li>
 <li>
 Ab initio Molecular Dynamics (AIMD)
 </li>
 <li>
 Time-dependent DFT (TDDFT) for excited states
 </li>
 <li>
 DFT+U for strongly correlated systems
 </li>
 <li>
 Hybrid functionals
 </li>
 <li>
 Hartree&ndash;Fock and post-HF methods (MP2, CCSD(T))
 
 
 </li>
 </ul>
 Molecular &amp; Statistical Simulations
 
 
 <ul>
 <li>
 Classical Molecular Dynamics (MD)
 </li>
 <li>
 Force-field development and validation
 </li>
 <li>
 Monte Carlo (MC) simulations
 </li>
 <li>
 Kinetic Monte Carlo (kMC) for surface reactions
 </li>
 <li>
 Machine Learning Force Fields ( Fairchem, Universal Forcefield, MACE)
 
 
 </li>
 </ul>
 Multiscale &amp; Mesoscale Modeling
 
 
 <ul>
 <li>
 Hierarchical nano &rarr; micro &rarr; meso &rarr; macro modeling
 </li>
 <li>
 QM&ndash;MM and QM&ndash;continuum coupling
 </li>
 <li>
 Microkinetic modeling and reaction networks
 </li>
 <li>
 Phase-field modeling
 </li>
 <li>
 Free energy methods (umbrella sampling, metadynamics)
 </li>
 <li>
 Materials Simulation &amp; Electronic Structure Modeling
 </li>
 <li>
 Bulk and surface DFT calculations
 </li>
 <li>
 k-point convergence, basis set testing
 </li>
 <li>
 Band structure, DOS, PDOS
 </li>
 <li>
 Phonons and lattice dynamics
 </li>
 <li>
 Elastic constants, thermal &amp; mechanical properties
 </li>
 <li>
 Defect formation energies
 </li>
 <li>
 Phase stability &amp; convex hulls
 </li>
 <li>
 Slab models and adsorption energies
 </li>
 <li>
 Reaction pathways using NEB / CI-NEB
 
 
 </li>
 </ul>
 Benchmarking &amp; Validation
 
 
 <li>
 Cross-code benchmarking across:
 </li>
 <ul>
 <li>
 DFT engines: VASP, Quantum ESPRESSO, CP2K, GPAW, CASTEP, Gaussian, ORCA
 </li>
 <li>
 MD engines: LAMMPS, GROMACS, NAMD
 </li>
 <li>
 ML potential
 </li>
 <li>
 Materials Informatics &amp; AI/ML
 </li>
 <li>
 Build ML models for materials &amp; catalyst property prediction:
 </li>
 <ul>
 <li>
 Fairchem, Universal Forcefield, CGCNN, MEGNet, SchNet, e3nn, NequIP
 </li>
 <li>
 Transformer &amp; foundation models for materials
 </li>
 </ul>
 <li>
 Curate datasets from:
 </li>
 <ul>
 <li>
 Materials Project, OQMD, NOMAD, JARVIS
 </li>
 </ul>
 <li>
 Develop ML surrogates for:
 </li>
 <ul>
 <li>
 Energy &amp; force prediction
 </li>
 <li>
 Bandgap estimation
 </li>
 <li>
 Thermal &amp; mechanical properties
 </li>
 <li>
 Catalyst screening &amp; ranking
 </li>
 </ul>
 <li>
 Integrate ML pipelines with:
 </li>
 <ul>
 <li>
 DFT / MD workflows
 </li>
 <li>
 Quantum simulation pipelines
 </li>
 </ul>
 <li>
 Simulation Workflow Engineering
 </li>
 <li>
 Build reproducible, automated workflows in Python for:
 </li>
 <ul>
 <li>
 High-throughput materials screening
 </li>
 <li>
 DFT&ndash;MD&ndash;CG-Mesoscale simulation pipelines
 </li>
 <li>
 Data extraction &amp; post-processing
 </li>
 </ul>
 <li>
 Develop modular tools for:
 </li>
 <ul>
 <li>
 Structure parsing (CIF, POSCAR, XYZ, PDB)
 </li>
 <li>
 Geometry builders &amp; surface generators
 </li>
 <li>
 Parameters generations
 </li>
 <li>
 Visualization (band structure, DOS, phonons, trajectories)
 </li>
 </ul>
 <li>
 Deploy workflows on:
 </li>
 <ul>
 <li>
 HPC clusters
 </li>
 <li>
 Cloud platforms (AWS, GCP)
 </li>
 <li>
 Containerized environments (Docker)
 </li>
 </ul>
 <li>
 Research, Collaboration &amp; Documentation
 </li>
 <li>
 Conduct literature reviews in:
 </li>
 <ul>
 <li>
 Computational materials
 </li>
 <li>
 Catalysis
 </li>
 <li>
 Semiconductors
 </li>
 <li>
 Alloy and Ceramics
 </li>
 <li>
 Polymers
 </li>
 <li>
 Quantum algorithms
 </li>
 </ul>
 <li>
 Design, execute, and analyze numerical experiments
 </li>
 <li>
 Prepare:
 </li>
 <ul>
 <li>
 Technical reports
 </li>
 <li>
 Internal whitepapers
 </li>
 <li>
 Presentations and datasets
 </li>
 </ul>
 <li>
 Collaborate closely with:
 </li>
 <ul>
 <li>
 Quantum hardware teams
 </li>
 <li>
 Algorithm developers
 </li>
 <li>
 AI/ML engineers
 
 </li>
 </ul>
 </ul>
 Specialization Tracks
 
 
 <ul>
 <li>
 DFT &amp; Electronic Structure Specialization
 </li>
 <li>
 Advanced XC functional selection &amp; benchmarking
 </li>
 <li>
 Strongly correlated systems (DFT+U, Hubbard models)
 </li>
 <li>
 Excited-state calculations (TDDFT, GW &ndash; exposure preferred)
 </li>
 <li>
 Defects, surfaces, and interfaces
 </li>
 <li>
 Electronic transport &amp; conductivity modeling
 </li>
 <li>
 Molecular Dynamics &amp; Classical Simulations
 </li>
 <li>
 Classical MD simulations (LAMMPS, GROMACS)
 </li>
 <li>
 Force-field parameterization &amp; validation
 </li>
 <li>
 Free energy calculations
 </li>
 <li>
 Reactive force fields (ReaxFF)
 </li>
 <li>
 ML-accelerated MD workflows
 </li>
 <li>
 Parameter generation for coarse-grained simulations
 </li>
 <li>
 Catalysis Specialization
 </li>
 <li>
 Heterogeneous, homogeneous &amp; electro-catalysis
 </li>
 <li>
 Reaction pathway identification
 </li>
 <li>
 Transition state searches (NEB, CI-NEB)
 </li>
 <li>
 Adsorption energies &amp; surface thermodynamics
 </li>
 <li>
 Microkinetic modeling
 </li>
 <li>
 Applications:
 </li>
 <ul>
 <li>
 OER, ORR, HER
 </li>
 <li>
 Photocatalysis
 </li>
 <li>
 Single-atom &amp; nanocluster catalysts
 </li>
 </ul>
 <li>
 Polymers &amp; Soft Matter Specialization
 </li>
 <li>
 DFT-based parameter extraction for polymers
 </li>
 <li>
 Multiscale polymer modeling (DFT, AA, CG)
 </li>
 <li>
 Dissipative Particle Dynamics (DPD)
 </li>
 <li>
 Monte Carlo Simulations
 </li>
 <li>
 Polymer blends, Polymer nanocomposites, surfactants, colloids
 </li>
 <li>
 Polymerization, degradation, crosslinking, morphology and aging studies
 </li>
 <li>
 Integration of DFT &rarr; MD &rarr; DPD&rarr;Phase field simulations pipelines
 </li>
 <li>
 Quantum Computing for Materials Simulation
 </li>
 <li>
 Map materials Hamiltonians to qubits:
 </li>
 <ul>
 <li>
 Jordan&ndash;Wigner, Bravyi&ndash;Kitaev, parity mappings
 </li>
 </ul>
 <li>
 Work on quantum algorithms including:
 </li>
 <ul>
 <li>
 VQE for correlated materials
 </li>
 <li>
 Subspace Quantum Diagonalization (SQD)
 </li>
 <li>
 qEOM for excited states
 </li>
 <li>
 Quantum Phase Estimation
 </li>
 <li>
 QITE / Quantum Monte Carlo
 </li>
 </ul>
 <li>
 Analyze:
 </li>
 <ul>
 <li>
 Qubit requirements
 </li>
 <li>
 Circuit depth
 </li>
 <li>
 Noise &amp; error budgets
 </li>
 </ul>
 <li>
 Design material-specific ans&auml;tze for NISQ devices and simulators
 
 </li>
 </ul>
 Software &amp; Programming Skills
 
 
 <ul>
 <li>
 DFT Codes: VASP, QE, CP2K, Gaussian, ORCA, CASTEP, ADF
 </li>
 <li>
 MD Codes: LAMMPS, GROMACS, NAMD, AMBER
 </li>
 <li>
 Visualization: VMD, VESTA, OVITO, Materials Studio, ASE
 </li>
 <li>
 Programming: Python (mandatory), Bash
 </li>
 <li>
 ML: PyTorch, TensorFlow, scikit-learn
 </li>
 <li>
 Infrastructure: HPC, MPI, Docker, Git, AWS / GCP
 
 
 </li>
 </ul>
 Soft Skills
 
 
 <ul>
 <li>
 Strong analytical and first-principles thinking
 </li>
 <li>
 Ability to design reproducible scientific workflows
 </li>
 <li>
 Clear scientific communication
 </li>
 <li>
 High ownership and curiosity-driven research mindset
 
 
 </li>
 </ul>
 
 Educational Qualifications
 
 
 
 <ul>
 <li>
 PhD (or pursuing PhD for intern role) in:
 </li>
 <ul>
 <li>
 Chemistry
 </li>
 <li>
 Materials Science
 </li>
 <li>
 Chemical Engineering
 </li>
 <li>
 Physics
 </li>
 <li>
 Computational Science or related STEM field
 </li>
 </ul>
 <li>
 Strong foundation in:
 </li>
 <ul>
 <li>
 Physical chemistry
 </li>
 <li>
 Quantum mechanics
 </li>
 <li>
 Statistical mechanics &amp; thermodynamics
 </li>
 </ul>
 <li>
 Specialization in computational chemistry / materials modeling strongly preferred
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Publications or strong computational project portfolio
 </li>
 <li>
 Experience with HPC &amp; large-scale simulations
 </li>
 <li>
 Prior work in:
 </li>
 <ul>
 <li>
 Materials discovery
 </li>
 <li>
 Catalysis
 </li>
 <li>
 Semiconductor
 </li>
 <li>
 Polymer modeling
 </li>
 <li>
 ML-driven materials science
 </li>
 </ul>
 <li>
 Exposure to quantum algorithms or hybrid quantum&ndash;classical workflows
 
 </li>
 </ul>
 Skills: ml,dft,materials,simulations,modeling
</div>

Computational Materials Discovery Scientist

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