6 Domain Adaptation Jobs

At Oracle Cloud Infrastructure (OCI), we build the future of the cloud for Enterprises as a diverse team of fellow creators and inventors. We act with the speed and attitude of a start-up, with the scale and customer-focus of the leading enterprise software company in the world. In the OCI AI Science org we are addressing exciting challenges at the intersection of artificial intelligence and cutting-edge cloud infrastructure. As a Data Scientist IV, you will lead the development of large-scale datasets and leverage machine learning and generative AI to advance Generative AI models. You will drive the design of benchmarking frameworks, define best practices for data quality and fairness, and partner with scientists, engineers, and product leaders to shape the org's data strategy. Key Responsibilities: Lead the design, development, and scaling of large, high-quality datasets to advance generative AI models in multimodal domains (e.g., text, vision, speech). Define data standards and best practices for acquisition, cleaning, augmentation, annotation, and evaluation to ensure fairness, diversity, and representativeness. Guide the integration of cutting-edge techniques (e.g., fine-tuning, RLHF, domain adaptation) into data generation and model alignment pipelines. Provide technical leadership in building scalable, reliable data pipelines and synthetic data platforms for production environments. Evaluate and operationalize research innovations, shaping how data preparation and generative AI methods transition into production-ready solutions. Partner with research, engineering, and product leaders to define long-term data strategy and accelerate the adoption of generative AI solutions at scale. Mentor and provide thought leadership to scientists and engineers, fostering a culture of data excellence and innovation Qualifications and Skills: Bachelors or Master's in Computer Science, Data Science, AI/ML, or related field with 6+ years of industry experience. Proficiency in Python and solid foundation in applied ML methods. Proficiency with Pytorch, Torchvision, OpenCV, and similar, as well as building and deploying DNN models in production. Experience building large-scale data pipelines for acquisition, cleaning, augmentation, and validation. Ability to evaluate datasets for distribution, diversity, anomalies and fairness to assess overall quality and suitability for generative AI. Experience with Computer Vision, NLP, Transformers, Large Language Models, Generative AI, optimizations around LLM training and serving. Experience with Multimodal models a bonus. Familiarity with advanced techniques (e.g., RLHF, domain adaptation, data augmentation) and their application in generative AI workflows. Proven track record of delivering scalable, data-centric ML solutions. Excellent communication and leadership skills, with experience mentoring junior scientists/engineers and presenting technical strategies to senior stakeholders. Career Level - IC4

At Oracle Cloud Infrastructure (OCI), we build the future of the cloud for Enterprises as a diverse team of fellow creators and inventors. We act with the speed and attitude of a start-up, with the scale and customer-focus of the leading enterprise software company in the world. In the OCI AI Science org we are addressing exciting challenges at the intersection of artificial intelligence and cutting-edge cloud infrastructure. As a Data Scientist IV, you will lead the development of large-scale datasets and leverage machine learning and generative AI to advance Generative AI models. You will drive the design of benchmarking frameworks, define best practices for data quality and fairness, and partner with scientists, engineers, and product leaders to shape the org's data strategy. Key Responsibilities: Lead the design, development, and scaling of large, high-quality datasets to advance generative AI models in multimodal domains (e.g., text, vision, speech). Define data standards and best practices for acquisition, cleaning, augmentation, annotation, and evaluation to ensure fairness, diversity, and representativeness. Guide the integration of cutting-edge techniques (e.g., fine-tuning, RLHF, domain adaptation) into data generation and model alignment pipelines. Provide technical leadership in building scalable, reliable data pipelines and synthetic data platforms for production environments. Evaluate and operationalize research innovations, shaping how data preparation and generative AI methods transition into production-ready solutions. Partner with research, engineering, and product leaders to define long-term data strategy and accelerate the adoption of generative AI solutions at scale. Mentor and provide thought leadership to scientists and engineers, fostering a culture of data excellence and innovation Qualifications and Skills: Bachelors or Master's in Computer Science, Data Science, AI/ML, or related field with 6+ years of industry experience. Proficiency in Python and solid foundation in applied ML methods. Proficiency with Pytorch, Torchvision, OpenCV, and similar, as well as building and deploying DNN models in production. Experience building large-scale data pipelines for acquisition, cleaning, augmentation, and validation. Ability to evaluate datasets for distribution, diversity, anomalies and fairness to assess overall quality and suitability for generative AI. Experience with Computer Vision, NLP, Transformers, Large Language Models, Generative AI, optimizations around LLM training and serving. Experience with Multimodal models a bonus. Familiarity with advanced techniques (e.g., RLHF, domain adaptation, data augmentation) and their application in generative AI workflows. Proven track record of delivering scalable, data-centric ML solutions. Excellent communication and leadership skills, with experience mentoring junior scientists/engineers and presenting technical strategies to senior stakeholders. Career Level - IC4

If you are a smart, self-motivated Machine Learning Scientist with a passion for advancing the field of Generative AI, an excellent opportunity awaits you. EXL, a rapidly expanding global digital data-led AI transformation solutions company, is seeking candidates with deep expertise in developing and fine-tuning Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic solutions, and knowledge graph technologies to drive innovative solutions in Generative AI. You will have the chance to be at the forefront of pioneering advancements in AI, working alongside bright minds in an exciting R&D environment to build cutting-edge capabilities that redefine the future of artificial intelligence. In this role, you will develop initiatives in the Generative AI domain, focusing on cutting-edge technologies like LLMs, RAG, and autonomous agents. You will design and implement advanced workflows for integrating LLMs into real-world applications across domains such as Finance, Insurance, and Healthcare. Additionally, you will drive the development of retrieval-augmented systems by combining LLMs with document retrieval, clustering, and search techniques. Keeping abreast of AI advancements is essential, as you will be required to read, adapt, and implement cutting-edge research to solve real-world challenges. Documenting research findings, methodologies, and implementations for internal and external stakeholders will also be part of your responsibilities. Qualifications: - Experience: 2-5 years in AI/ML research and development, with at least 1-2 years focusing on Generative AI, LLMs, or related fields. - Education: Masters or PhD in Computer Science, AI, or a related field from a top-tier institution is highly preferred. Required Skills: - Core Expertise: Proven experience with Large Language Models (e.g., GPT-4, BERT, LLaMA, PaLM) and fine-tuning them for domain-specific applications. In-depth knowledge of Retrieval-Augmented Generation workflows and hands-on experience with autonomous agents. - Tools & Frameworks: Proficiency in deep learning frameworks like TensorFlow, PyTorch, or Hugging Face Transformers. Experience with distributed training and optimization on GPUs and TPUs. Familiarity with cloud ecosystems (AWS, Azure, Google Cloud) practices for scalable deployment. - Research & Development: Ability to read and adapt cutting-edge research papers for applied solutions in LLMs and knowledge graphs. Expertise in domain adaptation, few-shot learning, and zero-shot reasoning. Strong understanding of generative models and their integration with LLMs. - Problem Solving: Demonstrated ability to address challenges in unstructured data processing, including NLP and multimodal scenarios. Experience with document retrieval, clustering, and unsupervised learning techniques. Preferred Skills: - Experience with LLM fine-tuning and building Agentic systems for domain LLMs. Experience with reinforcement learning and fine-tuning via RLHF. Knowledge of large-scale optimization methods and efficient model compression techniques. Strong collaboration and communication skills, with a proven ability to lead teams. Experience with MoE based architecture and knowledge of federated learning.,

About the Role QpiAI works at the intersection of AI and Quantum Computing, developing groundbreaking solutions to tackle some of the most complex challenges across various industries. Our team is dedicated to pushing the boundaries of innovation and delivering exceptional results to our enterprise clients, driving significant impact. We are seeking an exceptional Applied AI Researcher to join our innovative research team. This role will focus on advancing our AI capabilities through rigorous experimentation, multi-agent systems design, domain-specific scaffolding, and evaluation methodology development. Through this role you will get the opportunity to develop Compound AI systems impacting areas like Drug Discovery, Decision Intelligence, Chip Design , Quantum Computing etc. Key Responsibilities Design and implement novel multi-agent architectures that enable complex problem-solving and collaboration Develop domain-specific scaffolding techniques to guide AI systems in specialized environments Create and curate high-quality datasets for training and evaluating AI systems across various domains Establish comprehensive evaluation frameworks to measure system performance, robustness, and alignment Research and implement reinforcement learning approaches for agent behavior optimization Develop techniques for post-training optimization and domain adaptation Collaborate with cross-functional teams to translate research findings into practical applications Stay current with the latest advancements in AI research and contribute to the research community Document methodologies, findings, and best practices for internal knowledge sharing Qualifications Advanced degree (MS or PhD) in Computer Science, Machine Learning, AI, or related technical field 4+ years of experience in applied AI research Strong mathematical foundations in optimization, probability theory, and linear algebra Extensive expertise in Python programming and software development Strong proficiency in PyTorch or JAX for implementing and scaling AI models Experience in curating datasets, pre-training, and aligning multimodal small language models Demonstrated experience with various reinforcement learning post-training methods (RLHF, DPO, GRPO, SFT, etc.) Experience with prompting methods like Chain of Thought and curating Chain of Thought datasets Experience training small reasoning models for specific applications Familiarity with various alignment methods and value learning techniques Experience with multi-agent systems design and implementation Knowledge of distributed training on multi-node, multi-GPU clusters Experience with dataset curation, cleaning, and augmentation for diverse AI tasks Strong understanding of model evaluation metrics and methodology development Excellent communication skills and ability to present complex technical concepts clearly Preferred Experience Publication record in top-tier AI/ML conferences (NeurIPS, ICLR, AAMAS, ICML) Experience applying AI to scientific domains such as drug discovery, materials science, chip design, or mathematical reasoning Experience working with vision-language models and multimodal architectures Demonstrated success in developing efficient small language models for specialized tasks Research experience in open-endedness, including emergent behaviors, auto-curriculum learning, or open-world agent systems for design of experiments, hypothesis generation. Background in computational chemistry, physics, electrical engineering, or mathematics Experience with Kubernetes and containerized workflow orchestration Familiarity with ML infrastructure tools like Ray, Hydra, or MLflow Knowledge of interpretability methods and system analysis approaches Experience with domain adaptation for specialized scientific applications Open-source contributions to AI research or tooling Experience in developing custom GPU accelerated pipelines. Experience optimizing large-scale models for inference and deployment. What We Offer Opportunity to work on cutting-edge AI research with real-world impact Access to substantial computational resources for large-scale experimentation Collaborative environment with top researchers and engineers Flexibility to pursue novel research directions within the company&aposs mission Competitive compensation and benefits package Join us in shaping the future of intelligent systems and their applications across diverse scientific domains. Show more Show less

Vimaan is looking to onboard multiple Machine Learning Engineers in Bengaluru, India to drive the development of computer vision and machine learning algorithms to power our cutting-edge wall-to-wall warehouse inventory tracking and verification platform. This is a unique opportunity to exploit a treasure cove of unseen real-world data coming from a multi-camera perception system and develop large-scale computer vision and deep learning models to build a product that creates a disproportionate value for the warehouse industry. The role involves hands-on CV/ML software development and deployment from understanding the product requirements, defining Computer Vision functional specs to designing, developing, and deploying CV/ML models in production at scale. The ideal candidate for the Machine Learning Engineer position should have an MS in computer vision, machine learning, AI, applied mathematics, data science, or related technical fields, or a BS with 3+ years of experience in Computer Vision/Machine Learning. They should possess hands-on experience in developing new learning algorithms for computer vision tasks such as object detection, object tracking, instance segmentation, activity detection, depth estimation, optical flow, multi-view geometry, domain adaptation, adversarial and generative models, as well as representational learning with varying amounts of data. Knowledge of current deep learning literature and the mathematical foundations of machine learning is essential. Experience with popular object detection frameworks such as YOLO, SSD, or Faster R-CNN is considered a plus. The candidate should have the ability to train and debug deep learning systems, gain deep insights into data characteristics, and map those to appropriate model architectures. Experience working with inputs from multiple cameras and input modes is advantageous, as is experience in AI Infrastructure, Machine Learning Accelerators, On-Device Optimization, and training and deploying deep learning models on GPU-accelerated platforms. Strong programming skills with Python and experience with ML/DL frameworks like Tensorflow, Pytorch, etc., are required. Prior experience in deploying machine learning models in production environments, working with cloud platforms (e.g., AWS, Azure, Google Cloud), and familiarity with data pre-processing, augmentation, and visualization tools and libraries are necessary. The ideal candidate should be highly motivated, passionate, possess a strong work ethic, and be able to work effectively in a team or independently under supervision in a matrix management environment. Effective communication skills, problem-solving abilities, attention to detail, and a passion for staying at the forefront of technology advancements in machine learning and computer vision are key attributes. They should be able to work in a fast-paced, high-pressure startup environment, adapt to rapidly changing requirements, and convey complex technical concepts to non-technical stakeholders. The Machine Learning Engineer will be responsible for researching, designing, and developing machine learning algorithms and models for various tasks of detection, recognition, and classification for warehouse inventory management. They will implement and optimize deep learning architectures, explore techniques like transfer learning and data augmentation, guide annotation teams, curate and pre-process annotated datasets, collaborate with MLOps for integrating machine learning models into production systems, and conduct thorough performance analysis and evaluation of models using appropriate metrics and tools. It is essential for the Machine Learning Engineer to stay up-to-date with the latest advancements in machine learning and computer vision research and integrate relevant findings into the solutions developed at Vimaan. ABOUT VIMAAN Headquartered in Silicon Valley, with team members around the world, Vimaan is comprised of computer vision and hardware technologists, as well as warehousing domain experts with a successful history in technology startups. Vimaan's primary mission is to deliver computer vision and machine learning solutions to solve long-standing inventory visibility, accuracy, and quality challenges in the supply chain.,

Job Title: R&D Lead Location: India Employment Type: Full-time Department: EXL AI Innovation Reports To: Salary Range: About Us EXL (NASDAQ: EXLS) is a $7 billion public-listed NASDAQ company and a rapidly expanding global digital data-led AI transformation solutions company with double digit growth. EXL Digital unit spearheads the development and implementation of Generative AI (GenAI) business solutions for our clients in Banking & Finance, Insurance, and Healthcare. EXL has partnered with NVIDIA AI Foundry As a global leader in analytics, digital transformation, and AI innovation, EXL is committed to helping clients unlock the potential of generative AI to drive growth, efficiency, and innovation. Job Description: If you are a smart, self-motivated Machine Learning Scientist with a passion for advancing the field of Generative AI, we have an excellent opportunity for you. We are seeking candidates with deep expertise in developing and fine-tuning LLMs, RAG, agentic solutions, and knowledge graph technologies to drive innovative solutions in GenAI. You will be at the forefront of pioneering advancements in AI, working alongside some of the brightest minds in an exciting R&D environment to build cutting-edge capabilities that redefine the future of artificial intelligence. Job Responsibilities: Develope initiatives in the GenAI domain, focusing on cutting-edge technologies like Large Language Models, Retrieval-Augmented Generation, and autonomous agents. Design and implement advanced workflows for integrating LLMs into real-world applications across various domains such as Finance, Insurance, and Healthcare. Develop and fine-tune domain-specific LLMs to optimize performance, using techniques like prompt engineering, adapter-based tuning, or low-rank adaptation. Drive the development of retrieval-augmented systems by combining LLMs with document retrieval, clustering, and search techniques. Stay at the forefront of AI advancements by reading, adapting, and implementing cutting-edge research to solve real-world challenges. Document research findings, methodologies, and implementations for internal and external stakeholders. Qualifications: Experience: 2-5 years in AI/ML research and development, with at least 1-2 years focusing on GenAI, LLMs, or related fields. Education: Master's or PhD in Computer Science, AI, or a related field from a top-tier institution is highly preferred. Required Skills: Core Expertise: Proven experience with Large Language Models (e.g., GPT-4, BERT, LLaMA, PaLM) and fine-tuning them for domain-specific applications. In-depth knowledge of Retrieval-Augmented Generation workflows, including retrieval system design and document store integration. Hands-on experience developing and deploying autonomous agents for complex problem-solving tasks. Tools & Frameworks: Proficiency in deep learning frameworks like TensorFlow, PyTorch, or Hugging Face Transformers. Experience with distributed training and optimization on GPUs and TPUs. Familiarity with cloud ecosystems (AWS, Azure, Google Cloud) practices for scalable deployment. Research & Development: Ability to read and adapt cutting-edge research papers for applied solutions in LLMs and knowledge graphs. Expertise in domain adaptation, few-shot learning, and zero-shot reasoning. Strong understanding of generative models, including VAEs, GANs, or diffusion models, and their integration with LLMs. Problem Solving: Demonstrated ability to address challenges in unstructured data processing, including NLP and multimodal scenarios. Experience with document retrieval, clustering, and unsupervised learning techniques. Preferred Skills: Experience with LLM fine-tuning and building Agentic systems for domain LLMs. Experience with reinforcement learning and fine-tuning via RLHF (Reinforcement Learning with Human Feedback). Knowledge of large-scale optimization methods for model training and inference. Familiarity with knowledge distillation and efficient model compression techniques. Strong collaboration and communication skills, with a proven ability to lead teams. Experience with MoE based architecture and knowledge on federated learning.