6 Autoencoder Jobs

About Us: TrueFan is at the forefront of AI-driven content generation, leveraging cutting-edge generative models to build next-generation products. Our mission is to redefine content generation space through advanced AI technologies, including deep generative models, text-to-video and image-to-video and lipsync generation. We are looking for a Senior Machine Learning Engineer with deep expertise in generative AI, including diffusion models, 3D VAEs and GANs to drive our research and development in AI-generated content and real-time media synthesis . Job Description: As a Senior Machine Learning Engineer , you will be responsible for designing, developing, and deploying cutting-edge models for end-to-end content generation , including AI-driven image/video generation, lipsyncing, and multimodal AI systems . You will work on the latest advancements in deep generative modeling to create highly realistic and controllable AI-generated media. Responsibilities: Research & Develop : Design and implement state-of-the-art generative models , including Diffusion Models, 3D VAEs and GANs for AI-powered media synthesis . End-to-End Content Generation : Build and optimize AI pipelines for high-fidelity image/video generation and lipsyncing using diffusion and autoencoder models. Speech & Video Synchronization : Develop advanced lipsyncing and multimodal generation models that integrate speech, video, and facial animation for hyper-realistic AI-driven content. Real-Time AI Systems : Implement and optimize models for real-time content generation and interactive AI applications using efficient model architectures and acceleration techniques . Scaling & Production Deployment : Work closely with software engineers to deploy models efficiently on cloud-based architectures (AWS, GCP, or Azure) . Collaboration & Research : Stay ahead of the latest trends in deep generative models, diffusion models, and transformer-based vision systems to enhance AI-generated content quality. Experimentation & Validation : Design and conduct experiments to evaluate model performance, improve fidelity, realism, and computational efficiency , and refine model architectures. Code Quality & Best Practices : Participate in code reviews, improve model efficiency, and document research findings to enhance team knowledge-sharing and product development . Qualifications: Bachelor's or Master’s degree in Computer Science, Machine Learning, or a related field. 3+ years of experience working with deep generative models , including Diffusion Models, 3D VAEs, GANs and autoregressive models . Strong proficiency in Python and deep learning frameworks such as PyTorch. Expertise in multi-modal AI, text-to-image, and image-to-video generation , audio to lipsync Strong understanding of machine learning principles and statistical methods. Good to have experience in real-time inference optimization, cloud deployment, and distributed training . Strong problem-solving abilities and a research-oriented mindset to stay updated with the latest AI advancements. Familiarity with generative adversarial techniques, reinforcement learning for generative models, and large-scale AI model training . Preferred Qualifications: Experience with transformers and vision-language models (e.g., CLIP, BLIP, GPT-4V). Background in text-to-video generation, lipsync generation and real-time synthetic media applications . Experience in cloud-based AI pipelines (AWS, Google Cloud, or Azure) and model compression techniques (quantization, pruning, distillation) . Contributions to open-source projects or published research in AI-generated content, speech synthesis, or video synthesis . How to Apply: Interested candidates should submit their resume and a cover letter detailing their experience with generative models and their contributions to relevant projects at meghna.sidda@true-fan.in

Job Description: As a Senior Machine Learning Engineer , you will be responsible for designing, developing, and deploying cutting-edge models for end-to-end content generation , including AI-driven image/video generation, lipsyncing, and multimodal AI systems . You will work on the latest advancements in deep generative modeling to create highly realistic and controllable AI-generated media. Responsibilities: Research & Develop : Design and implement state-of-the-art generative models , including Diffusion Models, 3D VAEs and GANs for AI-powered media synthesis . End-to-End Content Generation : Build and optimize AI pipelines for high-fidelity image/video generation and lipsyncing using diffusion and autoencoder models. Speech & Video Synchronization : Develop advanced lipsyncing and multimodal generation models that integrate speech, video, and facial animation for hyper-realistic AI-driven content. Real-Time AI Systems : Implement and optimize models for real-time content generation and interactive AI applications using efficient model architectures and acceleration techniques . Scaling & Production Deployment : Work closely with software engineers to deploy models efficiently on cloud-based architectures (AWS, GCP, or Azure) . Collaboration & Research : Stay ahead of the latest trends in deep generative models, diffusion models, and transformer-based vision systems to enhance AI-generated content quality. Experimentation & Validation : Design and conduct experiments to evaluate model performance, improve fidelity, realism, and computational efficiency , and refine model architectures. Code Quality & Best Practices : Participate in code reviews, improve model efficiency, and document research findings to enhance team knowledge-sharing and product development . Qualifications: Bachelor's or Master’s degree in Computer Science, Machine Learning, or a related field. 3+ years of experience working with deep generative models , including Diffusion Models, 3D VAEs, GANs and autoregressive models . Strong proficiency in Python and deep learning frameworks such as PyTorch. Expertise in multi-modal AI, text-to-image, and image-to-video generation , audio to lipsync Strong understanding of machine learning principles and statistical methods. Good to have experience in real-time inference optimization, cloud deployment, and distributed training . Strong problem-solving abilities and a research-oriented mindset to stay updated with the latest AI advancements. Familiarity with generative adversarial techniques, reinforcement learning for generative models, and large-scale AI model training . Preferred Qualifications: Experience with transformers and vision-language models (e.g., CLIP, BLIP, GPT-4V). Background in text-to-video generation, lipsync generation and real-time synthetic media applications . Experience in cloud-based AI pipelines (AWS, Google Cloud, or Azure) and model compression techniques (quantization, pruning, distillation) . Contributions to open-source projects or published research in AI-generated content, speech synthesis, or video synthesis .

As a Senior Machine Learning Engineer, you will be responsible for designing, developing, and deploying cutting-edge models for end-to-end content generation, including AI-driven image/video generation, lip syncing, and multimodal AI systems. You will work on the latest advancements in deep generative modeling to create highly realistic and controllable AI-generated media. Responsibilities Research and Develop: Design and implement state-of-the-art generative models, including Diffusion Models, 3D VAEs, and GANs for AI-powered media synthesis. End-to-End Content Generation: Build and optimize AI pipelines for high-fidelity image/video generation and lip syncing using diffusion and autoencoder models. Speech and Video Synchronization: Develop advanced lip-syncing and multimodal generation models that integrate speech, video, and facial animation for hyper-realistic AI-driven content. Real-Time AI Systems: Implement and optimize models for real-time content generation and interactive AI applications using efficient model architectures and acceleration techniques. Scaling and Production Deployment: Work closely with software engineers to deploy models efficiently on cloud-based architectures (AWS, GCP, or Azure). Collaboration and Research: Stay ahead of the latest trends in deep generative models, diffusion models, and transformer-based vision systems to enhance AI-generated content quality. Experimentation and Validation: Design and conduct experiments to evaluate model performance, improve fidelity, realism, and computational efficiency, and refine model architectures. Code Quality and Best Practices: Participate in code reviews, improve model efficiency, and document research findings to enhance team knowledge-sharing and product development. Requirements Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field. 3+ years of experience working with deep generative models, including Diffusion Models, 3D VAEs, GANs, and autoregressive models. Strong proficiency in Python and deep learning frameworks such as PyTorch. Expertise inmulti-modal AI, text-to-image, and image-to-video generation, audio to lipsync Strong understanding of machine learning principles and statistical methods. Good to have experience in real-time inference optimization, cloud deployment, and distributed training. Strong problem-solving abilities and a research-oriented mindset to stay updated with the latest AI advancements. Familiarity with generative adversarial techniques, reinforcement learning for generative models, and large-scale AI model training. Preferred Qualifications Experience with transformers and vision-language models(e. g., CLIP, BLIP, GPT-4V). Background in text-to-video generation, lip-sync generation, and real-time synthetic media applications. Experience in cloud-based AI pipelines (AWS, Google Cloud, or Azure) and model compression techniques (quantization, pruning, distillation). Contributions to open-source projects or published research in AI-generated content, speech synthesis, or video synthesis. This job was posted by Meghna Sidda from TrueFan.

Title : Data Scientist Experience : 4 to 6 years Company : Scry AI Skill Sets : GEN AI (LLM), Python, Scikit-learn, SQL, k-means, decision trees, random forests, neural networks, genetic algorithms About The Role Design and develop Predictive Analytics and Prescriptive Analytics architecture and features for artificial intelligence software solutions, including developing machine learning and statistical models to perform big data analysis. Duties Will Include The Following Research, design and develop algorithms and techniques to perform data manipulation and transformation; build generative and discriminative models to perform clustering, classification, regression, and recommendation systems. Design and build custom machine learning models. Building GEN AI (LLM) pipelines using LangChain, LlamaIndex, Vector DBs, embedding generation and Hugging Face. Build and implement machine learning algorithms to plan, create, coordinate, and deploy business information solutions; gather business requirements and technical specifications for developing analytics solutions. Develop artificial intelligence and machine learning algorithms. Custom design and implement various techniques in machine learning such as support vector machines, neural networks, genetic algorithms, principal component analysis, cluster analysis, k-means, decision trees, random forests, regression analysis, Bayesian analysis, Na- ve Bayes, and deep learning. Analyze large, complex and multi-dimensional data sets. Develop analytic solutions using R, Python, Scikit-learn, SQL and similar statistical tools and frameworks. Utilize Deep Learning neural networks including RNN, CNN, LSTM, and Autoencoder to predict the behavior and performance of businesses. Requirements Masters degree (or equivalent-) in Data Informatics, Computer Science, Applied Mathematics, Statistics, Analytics or a related field such as Computer Science and Engineering. Minimum 4+ years of relevant experience in the job offered or as a Software Engineer, Software Engineering Analyst, Application Developer, Programmer Analyst. Minimum 2 years of experience with algorithm design and development; software architecture development; data engineering, data structures, and data visualization; statistical data analysis. Artificial Intelligence (AI) and deep learning techniques; feature synthesis, engineering, and hyper-parameter tuning. Natural Language Processing; Machine Learning, including Bayesian modeling, multivariate and logistic regression, support vector machines, cluster analysis, decision and regression trees, random forest, neural networks and ensemble methods; analyzing large, complex, multi-dimensional data sets and developing analytic solutions. Experience working with structured and unstructured data (text, image). Working experience with unstructured data such as audios and videos is not mandatory but will be an add-on advantage. Experience in predictive analytics using R, Python, Scikit-learn, and SQL, PostgreSQL statistical tools; Spark, Hadoop, Unix O/S, GitHub; and programming languages Java, Python, R. Our Perfect Candidate Is Someone That Is proactive and an independent problem solver. Is a constant learner. We are a fast-growing company. We want you to grow with us! Is a team player and good communicator. (ref:hirist.tech) Show more Show less

Job Description Vice President, Data Management & Quantitative Analysis I At BNY, our culture empowers you to grow and succeed. As a leading global financial services company at the center of the world’s financial system we touch nearly 20% of the world’s investible assets. Every day around the globe, our 50,000+ employees bring the power of their perspective to the table to create solutions with our clients that benefit businesses, communities and people everywhere. We continue to be a leader in the industry, awarded as a top home for innovators and for creating an inclusive workplace. Through our unique ideas and talents, together we help make money work for the world. This is what is all about. We’re seeking a future team member for the role of Vice President I to join our Data Management & Quantitative Analysis team. This role is located in Pune, MH or Chennai, TN (Hybrid). In this role, you’ll make an impact in the following ways: BNY Data Analytics Reporting and Transformation (“DART”) has grown rapidly and today it represents a highly motivated and engaged team of skilled professionals with expertise in financial industry practices, reporting, analytics, and regulation. The team works closely with various groups across BNY to support the firm’s Capital Adequacy, Counterparty Credit as well as Enterprise Risk modelling and data analytics; alongside support for the annual Comprehensive Capital Analysis and Review (CCAR) Stress Test. The Counterparty Credit Risk Data Analytics Team within DART designs and develops data-driven solutions aimed at strengthening the control framework around our risk metrics and reporting. For the Counterparty Credit Risk Data Analytics Team, we are looking for a Counterparty Risk Analytics Developer to support our Counterparty Credit Risk control framework. Develop analytical tools using SQL & Python to drive business insights Utilize outlier detection methodologies to identify data anomalies in the financial risk space, ensuring proactive risk management Analyze business requirements and translate them into practical solutions, developing data-driven controls to mitigate potential risks Plan and execute projects from concept to final implementation, demonstrating strong project management skills Present solutions to senior stakeholders, effectively communicating technical concepts and results Collaborate with internal and external auditors and regulators to ensure compliance with prescribed standards, maintaining the highest level of integrity and transparency. To be successful in this role, we’re seeking the following: A Bachelor's degree in Engineering, Computer Science, Data Science, or a related discipline (Master's degree preferred) At least 3 years of experience in a similar role or in Python development/data analytics Strong proficiency in Python (including data analytics, data visualization libraries) and SQL, basic knowledge of HTML and Flask. Ability to partner with technology and other stakeholders to ensure effective functional requirements, design, construction, and testing Knowledge of financial risk concepts and financial markets is strongly preferred Familiarity with outlier detection techniques (including Autoencoder method, random forest, etc.), clustering (k-means, etc.), and time series analysis (ARIMA, EWMA, GARCH, etc.) is a plus. Practical experience working with Python (Pandas, NumPy, Matplolib, Plotly, Dash, Scikit-learn, TensorFlow, Torch, Dask, Cuda) Intermediate SQL skills (including querying data, joins, table creation, and basic performance optimization techniques) Knowledge of financial risk concepts and financial markets Knowledge of outlier detection techniques, clustering, and time series analysis Strong project management skills Show more Show less

At BNY, our culture empowers you to grow and succeed. As a leading global financial services company at the center of the world’s financial system we touch nearly 20% of the world’s investible assets. Every day around the globe, our 50,000+ employees bring the power of their perspective to the table to create solutions with our clients that benefit businesses, communities and people everywhere. We continue to be a leader in the industry, awarded as a top home for innovators and for creating an inclusive workplace. Through our unique ideas and talents, together we help make money work for the world. This is what is all about. We’re seeking a future team member for the role of Vice President I to join our Data Management & Quantitative Analysis team. This role is located in Pune, MH or Chennai, TN (Hybrid). In this role, you’ll make an impact in the following ways: BNY Data Analytics Reporting and Transformation (“DART”) has grown rapidly and today it represents a highly motivated and engaged team of skilled professionals with expertise in financial industry practices, reporting, analytics, and regulation. The team works closely with various groups across BNY to support the firm’s Capital Adequacy, Counterparty Credit as well as Enterprise Risk modelling and data analytics; alongside support for the annual Comprehensive Capital Analysis and Review (CCAR) Stress Test. The Counterparty Credit Risk Data Analytics Team within DART designs and develops data-driven solutions aimed at strengthening the control framework around our risk metrics and reporting. For the Counterparty Credit Risk Data Analytics Team, we are looking for a Counterparty Risk Analytics Developer to support our Counterparty Credit Risk control framework. Develop analytical tools using SQL & Python to drive business insights Utilize outlier detection methodologies to identify data anomalies in the financial risk space, ensuring proactive risk management Analyze business requirements and translate them into practical solutions, developing data-driven controls to mitigate potential risks Plan and execute projects from concept to final implementation, demonstrating strong project management skills Present solutions to senior stakeholders, effectively communicating technical concepts and results Collaborate with internal and external auditors and regulators to ensure compliance with prescribed standards, maintaining the highest level of integrity and transparency. To be successful in this role, we’re seeking the following: A Bachelor's degree in Engineering, Computer Science, Data Science, or a related discipline (Master's degree preferred) At least 3 years of experience in a similar role or in Python development/data analytics Strong proficiency in Python (including data analytics, data visualization libraries) and SQL, basic knowledge of HTML and Flask. Ability to partner with technology and other stakeholders to ensure effective functional requirements, design, construction, and testing Knowledge of financial risk concepts and financial markets is strongly preferred Familiarity with outlier detection techniques (including Autoencoder method, random forest, etc.), clustering (k-means, etc.), and time series analysis (ARIMA, EWMA, GARCH, etc.) is a plus. Practical experience working with Python (Pandas, NumPy, Matplolib, Plotly, Dash, Scikit-learn, TensorFlow, Torch, Dask, Cuda) Intermediate SQL skills (including querying data, joins, table creation, and basic performance optimization techniques) Knowledge of financial risk concepts and financial markets Knowledge of outlier detection techniques, clustering, and time series analysis Strong project management skills Show more Show less