Machine Learning Intern Co Op in India

WHO ARE WE? We are a bunch of super enthusiastic, passionate, and highly driven people, working to achieve a common goal! We believe that work and the workplace should be joyful and always buzzing with energy! CloudSEK , one of India’s most trusted Cyber security product companies, is on a mission to build the world’s fastest and most reliable AI technology that identifies and resolves digital threats in real-time. The central proposition is leveraging Artificial Intelligence and Machine Learning to create a quick and reliable analysis and alert system that provides rapid detection across multiple internet sources, precise threat analysis, and prompt resolution with minimal human intervention. Founded in 2015, headquartered at Singapore, we are proud to say that we’ve grown at a frenetic pace and have been able to achieve some accolades along the way, including: CloudSEK’s Product Suite: CloudSEK XVigil constantly maps a customer’s digital assets, identifies threats and enriches them with cyber intelligence, and then provides workflows to manage and remediate all identified threats including takedown support. A powerful Attack Surface Monitoring tool that gives visibility and intelligence on customers’ attack surfaces. CloudSEK's BeVigil uses a combination of Mobile, Web, Network and Encryption Scanners to map and protect known and unknown assets. CloudSEK’s Contextual AI SVigil identifies software supply chain risks by monitoring Software, Cloud Services, and third-party dependencies. CloudSEK’s AIVigil is an AI-native Attack Surface Monitoring platform that continuously discovers, monitors, and secures exposed AI infrastructure, MCP servers, leaked AI credentials, vector databases, agentic workflows, and shadow AI across the internet. Key Milestones: 2016 : Launched our first product. 2018 : Secured Pre-series A funding. 2019 : Expanded operations to India, Southeast Asia, and the Americas. 2020 : Won the NASSCOM-DSCI Excellence Award for Security Product Company

At FourKites we have the opportunity to tackle complex challenges with real-world impacts. Whether it’s medical supplies from Cardinal Health or groceries for Walmart, the FourKites platform helps customers operate global supply chains that are efficient, agile and sustainable. Join a team of curious problem solvers that celebrates differences, leads with empathy and values inclusivity . Are you looking to be an AI & SAAS Support Intern on a growing team? If so, this may be the role for you! You will be the face of FourKites to our customers—their main point of contact for everything they need. From helping customers track urgent shipments to providing assistance to carriers hauling the loads, you will be the product expert and go-to person to help users when they reach out. This internship is designed for hands-on, experiential learning —you'll get real exposure to how a growing SaaS platform operates, paired with the chance to work on special AI projects alongside our product and engineering teams. What you'll be doing: First point of contact for FourKites customers, partners & consultants to research, troubleshoot, replicate, and test product problems and bugs Track issue progress and provide regular updates to clients until incidents are resolved Interface with Development, QA, and Consulting Services as needed to resolve customer issues Contribute to special AI-driven initiatives , applying a curious, experimental mindset to explore how AI can improve support workflows and customer outcomes Be part of weekend rotation for 24/7 customer emergencies About the team: Our product and engineering teams are dedicated to providing the industry's best-in-class end-to-end supply chain visibility platform. We are committed to building a high-performing team with software craftsman-thinking who are dedicated to developing and enhancing our core platform, and we want you to be a central part of it! We help companies transform their end-to-end supply chain with real-

WHO ARE WE? We are a bunch of super enthusiastic, passionate, and highly driven people, working to achieve a common goal! We believe that work and the workplace should be joyful and always buzzing with energy! CloudSEK , one of India’s most trusted Cyber security product companies, is on a mission to build the world’s fastest and most reliable AI technology that identifies and resolves digital threats in real-time. The central proposition is leveraging Artificial Intelligence and Machine Learning to create a quick and reliable analysis and alert system that provides rapid detection across multiple internet sources, precise threat analysis, and prompt resolution with minimal human intervention. Founded in 2015, headquartered at Singapore, we are proud to say that we’ve grown at a frenetic pace and have been able to achieve some accolades along the way, including: CloudSEK’s Product Suite: CloudSEK XVigil constantly maps a customer’s digital assets, identifies threats and enriches them with cyber intelligence, and then provides workflows to manage and remediate all identified threats including takedown support. A powerful Attack Surface Monitoring tool that gives visibility and intelligence on customers’ attack surfaces. CloudSEK's BeVigil uses a combination of Mobile, Web, Network and Encryption Scanners to map and protect known and unknown assets. CloudSEK’s Contextual AI SVigil identifies software supply chain risks by monitoring Software, Cloud Services, and third-party dependencies. CloudSEK’s AIVigil is an AI-native Attack Surface Monitoring platform that continuously discovers, monitors, and secures exposed AI infrastructure, MCP servers, leaked AI credentials, vector databases, agentic workflows, and shadow AI across the internet. Key Milestones: 2016 : Launched our first product. 2018 : Secured Pre-series A funding. 2019 : Expanded operations to India, Southeast Asia, and the Americas. 2020 : Won the NASSCOM-DSCI Excellence Award for Security Product Company

WHO ARE WE? We are a bunch of super enthusiastic, passionate, and highly driven people, working to achieve a common goal! We believe that work and the workplace should be joyful and always buzzing with energy! CloudSEK , one of India’s most trusted Cyber security product companies, is on a mission to build the world’s fastest and most reliable AI technology that identifies and resolves digital threats in real-time. The central proposition is leveraging Artificial Intelligence and Machine Learning to create a quick and reliable analysis and alert system that provides rapid detection across multiple internet sources, precise threat analysis, and prompt resolution with minimal human intervention. Founded in 2015, headquartered at Singapore, we are proud to say that we’ve grown at a frenetic pace and have been able to achieve some accolades along the way, including: CloudSEK’s Product Suite: CloudSEK XVigil constantly maps a customer’s digital assets, identifies threats and enriches them with cyber intelligence, and then provides workflows to manage and remediate all identified threats including takedown support. A powerful Attack Surface Monitoring tool that gives visibility and intelligence on customers’ attack surfaces. CloudSEK's BeVigil uses a combination of Mobile, Web, Network and Encryption Scanners to map and protect known and unknown assets. CloudSEK’s Contextual AI SVigil identifies software supply chain risks by monitoring Software, Cloud Services, and third-party dependencies. CloudSEK’s AIVigil is an AI-native Attack Surface Monitoring platform that continuously discovers, monitors, and secures exposed AI infrastructure, MCP servers, leaked AI credentials, vector databases, agentic workflows, and shadow AI across the internet. Key Milestones: 2016 : Launched our first product. 2018 : Secured Pre-series A funding. 2019 : Expanded operations to India, Southeast Asia, and the Americas. 2020 : Won the NASSCOM-DSCI Excellence Award for Security Product Company

WHO ARE WE? We are a bunch of super enthusiastic, passionate, and highly driven people, working to achieve a common goal! We believe that work and the workplace should be joyful and always buzzing with energy! CloudSEK , one of India’s most trusted Cyber security product companies, is on a mission to build the world’s fastest and most reliable AI technology that identifies and resolves digital threats in real-time. The central proposition is leveraging Artificial Intelligence and Machine Learning to create a quick and reliable analysis and alert system that provides rapid detection across multiple internet sources, precise threat analysis, and prompt resolution with minimal human intervention. Founded in 2015, headquartered at Singapore, we are proud to say that we’ve grown at a frenetic pace and have been able to achieve some accolades along the way, including: CloudSEK’s Product Suite: CloudSEK XVigil constantly maps a customer’s digital assets, identifies threats and enriches them with cyber intelligence, and then provides workflows to manage and remediate all identified threats including takedown support. A powerful Attack Surface Monitoring tool that gives visibility and intelligence on customers’ attack surfaces. CloudSEK's BeVigil uses a combination of Mobile, Web, Network and Encryption Scanners to map and protect known and unknown assets. CloudSEK’s Contextual AI SVigil identifies software supply chain risks by monitoring Software, Cloud Services, and third-party dependencies. CloudSEK’s AIVigil is an AI-native Attack Surface Monitoring platform that continuously discovers, monitors, and secures exposed AI infrastructure, MCP servers, leaked AI credentials, vector databases, agentic workflows, and shadow AI across the internet. Key Milestones: 2016 : Launched our first product. 2018 : Secured Pre-series A funding. 2019 : Expanded operations to India, Southeast Asia, and the Americas. 2020 : Won the NASSCOM-DSCI Excellence Award for Security Product Company

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. About Okta: Okta is the leading independent provider of identity solutions for enterprises. With a deep commitment to innovation and customer success, we power and secure digital transformation for the world's biggest brands. We're scaling rapidly, and we’re seeking talented individuals to join us in enabling secure and seamless digital experiences. One of the most important components of our operations is an efficient financial system, including seamless processes for the Record-to-Report (R2R) cycle. As a NetSuite R2R Analyst, your role will support and strengthen Okta's financial systems and processes to ensure accuracy, efficiency, and scalability. This is a potential Contract to Hire position. Roles & Responsibilities: Act as the subject matter expert (SME) for NetSuite functionality in Record-to-Report (R2R) processes, maintaining workflows, configurations, and data integrity to enhance efficiency and compliance (e.g., SOX, GAAP). Lead or participate in R2R-related projects, including implementations, migrations, process improvements, customizations, upgrades, and integrations with NetSuite’s financial modules. Support month-end, quarter-end, and year-end close processes by ensuring timely reconciliations, reporting, general ledger accuracy, consolidations, and inter company accounting. Partner with finance, IT, and external vendors to provide NetSuite support, resolve issues, streamline processes, evaluate automation opportunities, and deliver us

Role Purpose We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native: Hands-on ex

Machine Learning Engineer IV – (Computer Vision) We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Strong industry experience in Machine Learning, dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native: Hands-on exper

Role Purpose: The Machine Learning Engineer IV will play a critical role in advancing Jumio's Fraud team's mission to develop and enhance state-of-the-art solutions for fraud detection for ID verification purposes. This role is essential for ensuring the highest standards of security and user verification through the application of advanced machine learning and deep learning techniques, ultimately contributing to Jumio's leadership in the online identity verification, eKYC, and AML solutions market Role Value: As a Machine Learning Engineer IV at Jumio, you will have the opportunity to significantly impact the security and user experience of our ID verification solutions. Your expertise in deep learning and computer vision will drive the development of innovative algorithms that keep Jumio at the forefront of the industry. By deploying and maintaining these models in production, you will ensure the robustness and reliability of our solutions, supporting our clients across diverse industries such as Financial Services, Travel, Sharing Economy, Fintech, and Gaming. Your contributions will be pivotal in maintaining Jumio's reputation as the leading provider of online identity verification solutions, helping to meet the growing demand for secure and seamless user verification globally. Example Responsibilities . Develop, maintain, and own key fraudulent CV models of Jumio, which shapes the whole fraud product offering of Jumio. Design and implement machine learning, deep learning, classical CV focused on fraud detection. Research to support the deployment of the advanced algorithms. Deploy models as AWS SageMaker endpoints or directly onto devices. Stay updated with the latest advancements in machine learning, deep learning, and computer vision by engaging with academic papers and attending industry conferences. Work collaboratively with other engineers and product managers in an Agile development environment. Experience and Qualifications Bach

Role Purpose: At Jumio, you will work for one of the market leaders in the global identity verification space that is helping to make the digital world a safer place for everyone. As a Software Development Engineer in the MLOpsTeam, you will develop the blueprint for highly scalable and performant ML model serving. Role Value: As a Software Engineer (SDE III), you will drive the continuous improvement of the infrastructure and applications to manage the lifecycle of ML assets (data, models) to better developer experience and strengthen governance capabilities. Secondly, you will design and implement robust ML infrastructure for model deployment, serving, and optimization. You will work on efficient CI/CD pipelines for ML models and leverage advanced compilers or hardware optimization to maximize inference performance while optimizing costs. We welcome you to challenge us to impact our software development processes and tools. Example Responsibilities: Upgrade ML assets (models, data) management systems for better developer experience and robust governance capabilities Build and optimize model serving infrastructure with a focus on inference latency and cost optimization Architect efficient inference pipelines that balance latency, throughput, and cost across various acceleration options Implement cost-efficient, enterprise-scale solutions Collaborate in a cross-functional, distributed team for continuous system improvement Work with MLEs, QA Engineers, and DevOps Engineers Evaluate and implement new technologies and tools Contribute to architectural decisions for distributed ML systems Experience and Qualifications : 5+ years of experience in software engineering with Python Experience with model lifecycle management (MLFlow, Weights & Biases or equivalent) Experience with data management ecosystem (quality, transformation, catalog) Experience with ML frameworks, particularly PyTorch Experience optimizing ML models with hardwar

Who are we: Graviton is a privately funded quantitative trading firm striving for excellence in financial markets research. We trade across a multitude of asset classes and trading venues using a gamut of concepts and techniques ranging from time series analysis, filtering, classification, stochastic models, pattern recognition, to statistical inference analyzing terabytes of data to come up with ideas to identify pricing anomalies in financial markets. As part of this team you will be tasked to apply machine learning and specifically deep learning techniques to trading problems while staying connected to broader research community. The researcher will put theory into practice and can immediately impact the global trading landscape with the expanding presence of Graviton in various markets. Description Lead research in applying machine learning to a wide variety of datasets and trading problems Follow latest developments in academic research and incorporating research techniques from different fields of applications to our problems Improve tick-by-tick order book based time series feature sets using latest preprocessing techniques Work on current and develop new deep learning models to exploit large pool of in-house features and computing infrastructure Develop scalable pipeline for building predictive models across global markets Discover and implement new sources of predictive alpha, verify that they improve existing models, and integrate them into the firm's strategy development pipeline Partner with quant researchers and software developers in implementation of conducted research to production using Python / C++ Advise infrastructure support team on latest developments on hardware and software to improve computing infrastructure for ML based research Qualifications Masters or PhD in Computer Science, Mathematics, Statistics, or a related field At least two years of demonstrated experience of ML/AI research in a professional setting or at a repu

A Career with Point72's Technology Team As Point72 reimagines the future of investing, our Technology group is constantly improving our company’s IT infrastructure, positioning us at the forefront of a rapidly evolving technology landscape. We’re a team of experts experimenting, discovering new ways to harness the power of open source solutions, and embracing enterprise agile methodology. We encourage professional development to ensure you bring innovative ideas to our products while satisfying your own intellectual curiosity. What you'll do Lead the design, development, and operation of scalable, enterprise-grade AI/ML architectures and systems with a strong emphasis on reliability, availability, and performance. Lead and mentor a team of engineers, driving technical direction, code quality, and iterative delivery of large-scale solutions. Partner closely with data scientists, engineers, product teams, and compliance to integrate AI/ML solutions into existing and new products. Own the end-to-end lifecycle of GenAI services, including LLM inference, model serving, and proxy/gateway layers that support multiple downstream applications. Define and uphold engineering best practices around observability, scalability, security, and cost efficiency for AI/ML platforms. Evaluate tools, technologies, and processes to ensure the highest quality and performance of AI/ML systems. Stay abreast of the latest advancements in AI/ML technologies and methodologies, and translate them into pragmatic solutions for the business. Ensure compliance with industry standards and best practices in AI/ML. What's required Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 10+ years of experience in software/AI/ML engineering, with a proven track record of successful delivery of complex, production-grade systems. Demonstrated experience building large-scale enterprise-grade services with high reliability, availability, and observability (SLO/SLA-driven en

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and