9 Clip Jobs

As a Senior Machine Learning Engineer at TrueFan, you will be at the forefront of AI-driven content generation, leveraging cutting-edge generative models to build next-generation products. Your mission will be to redefine the content generation space through advanced AI technologies, including deep generative models, text-to-video, image-to-video, and lipsync generation. Your responsibilities will include designing, developing, and deploying cutting-edge models for end-to-end content generation. This will involve working on the latest advancements in deep generative modeling to create highly realistic and controllable AI-generated media. You will research and develop state-of-the-art generative models such as Diffusion Models, 3D VAEs, and GANs for AI-powered media synthesis. Additionally, you will build and optimize AI pipelines for high-fidelity image/video generation and lipsyncing using diffusion and autoencoder models. Furthermore, you will be responsible for developing advanced lipsyncing and multimodal generation models that integrate speech, video, and facial animation for hyper-realistic AI-driven content. Your role will also involve implementing and optimizing models for real-time content generation and interactive AI applications using efficient model architectures and acceleration techniques. Collaboration with software engineers to deploy models efficiently on cloud-based architectures will be a key aspect of your work. To qualify for this role, you should have a Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field, along with 3+ years of experience working with deep generative models like Diffusion Models, 3D VAEs, GANs, and autoregressive models. Proficiency in Python and deep learning frameworks such as PyTorch is essential. Strong problem-solving abilities, a research-oriented mindset, and familiarity with generative adversarial techniques are also required. Preferred qualifications include experience with transformers and vision-language models, background in text-to-video generation and lipsync generation, expertise in cloud-based AI pipelines, and contributions to open-source projects or published research in AI-generated content. If you are passionate about AI-driven content generation and have a strong background in generative AI, this is the perfect opportunity for you to drive research and development in AI-generated content and real-time media synthesis at TrueFan.,

As a Senior Machine Learning Engineer, you will have the exciting opportunity to be involved in designing, developing, and deploying cutting-edge models for end-to-end content generation. This includes working on AI-driven image/video generation, lip syncing, and multimodal AI systems. You will be at the forefront of the latest advancements in deep generative modeling, striving to create highly realistic and controllable AI-generated media. Your responsibilities will encompass researching and developing state-of-the-art generative models like Diffusion Models, 3D VAEs, and GANs for AI-powered media synthesis. You will focus on building and optimizing AI pipelines for high-fidelity image/video generation and lip syncing. Additionally, you will be tasked with developing advanced lip-syncing and multimodal generation models that integrate speech, video, and facial animation for hyper-realistic AI-driven content. Implementing and optimizing models for real-time content generation and interactive AI applications using efficient model architectures and acceleration techniques will also be part of your role. Collaboration with software engineers to deploy models efficiently on cloud-based architectures (AWS, GCP, or Azure) will be crucial. Staying updated with the latest trends in deep generative models, diffusion models, and transformer-based vision systems to enhance AI-generated content quality will be an essential aspect of the role. Furthermore, designing and conducting experiments to evaluate model performance, improve fidelity, realism, and computational efficiency, as well as refining model architectures will be expected. Active participation in code reviews, improving model efficiency, and documenting research findings to enhance team knowledge-sharing and product development will also be part of your responsibilities. To qualify for this role, you should hold a Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field. You should have a minimum of 3 years of experience working with deep generative models, such as Diffusion Models, 3D VAEs, GANs, and autoregressive models. Proficiency in Python and deep learning frameworks like PyTorch is essential. Expertise in multi-modal AI, text-to-image, and image-to-video generation, as well as audio to lip sync, is required. A strong understanding of machine learning principles and statistical methods is necessary. It would be beneficial 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 are qualities that would be valued. Familiarity with generative adversarial techniques, reinforcement learning for generative models, and large-scale AI model training will also be beneficial. Preferred qualifications include experience with transformers and vision-language models (e.g., CLIP, BLIP, GPT-4V), a background in text-to-video generation, lip-sync generation, and real-time synthetic media applications, as well as 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 would be advantageous.,

You will be responsible for owning the full ML stack that is capable of transforming raw dielines, PDFs, and e-commerce images into a self-learning system that can read, reason about, and design packaging artwork. This includes building data-ingestion & annotation pipelines for SVG/PDF to JSON conversion, designing and modifying model heads using technologies such as LayoutLM-v3, CLIP, GNNs, and diffusion LoRAs, training & fine-tuning on GPUs, as well as shipping inference APIs and evaluation dashboards. Your daily tasks will involve close collaboration with packaging designers and a product manager, establishing you as the technical authority on all aspects of deep learning within this domain. Your key responsibilities will be divided into three main areas: **Area Tasks:** - Data & Pre-processing (40%): Writing robust Python scripts for parsing PDF, AI, SVG files, extracting text, colour separations, images, and panel polygons. Implementing tools like Ghostscript, Tesseract, YOLO, and CLIP pipelines. Automating synthetic-copy generation for ECMA dielines and maintaining vocabulary YAMLs & JSON schemas. - Model R&D (40%): Modifying LayoutLM-v3 heads, building panel-encoder pre-train models, adding Graph-Transformer & CLIP-retrieval heads, and running experiments, hyper-param sweeps, ablations to track KPIs such as IoU, panel-F1, colour recall. - MLOps & Deployment (20%): Packaging training & inference into Docker/SageMaker or GCP Vertex jobs, maintaining CI/CD, experiment tracking, serving REST/GraphQL endpoints, and implementing an active-learning loop for designer corrections. **Must-Have Qualifications:** - 5+ years of Python experience and 3+ years of deep-learning experience with PyTorch, Hugging Face. - Hands-on experience with Transformer-based vision-language models and object-detection pipelines. - Proficiency in working with PDF/SVG tool-chains, designing custom heads/loss functions, and fine-tuning pre-trained models on limited data. - Strong knowledge of Linux, GPU, graph neural networks, and relational transformers. - Proficient in Git, code review discipline, and writing reproducible experiments. **Nice-to-Have:** - Knowledge of colour science, multimodal retrieval, diffusion fine-tuning, or packaging/CPG industry exposure. - Experience with vector search tools, AWS/GCP ML tooling, and front-end technologies like Typescript/React. You will own a tool stack including DL frameworks like PyTorch, Hugging Face Transformers, torch-geometric, parsing/CV tools, OCR/detectors, retrieval tools like CLIP/ImageBind, and MLOps tools such as Docker, GitHub Actions, W&B or MLflow. In the first 6 months, you are expected to deliver a data pipeline for converting ECMA dielines and PDFs, a panel-encoder checkpoint, an MVP copy-placement model, and a REST inference service with a designer preview UI. You will report to the Head of AI or CTO and collaborate with a front-end engineer, a product manager, and two packaging-design SMEs.,

Role & responsibilities Minimum of 2 to 5 years of experience in wiring harness design, with at least 2 years working in electric vehicle or automotive design in Schematics.. Proven experience with the design and development of electrical systems and wiring harnesses for vehicles (Conventional or EV, Hybrid, PHEV, Commercial vehicle or passenger vehicle ). Ensure wiring harness designs comply with all relevant standards, regulations, and best practices, including ISO 26262 (Functional Safety) and ISO 9001 or IATF 16949 (Quality Management). Use advanced CAD software - Capital XC to design and model wiring harnesses, ensuring high-quality designs Resource Design: Expertise in wiring harness design and CAD tools mainly with Capital XC/CHS Strong knowledge of electrical components, connectors, and vehicle power distribution systems. Understanding of industry standards for electrical design (ISO 26262, UL, etc.). Proficiency in using simulation and analysis tools for electrical system performance (e.g., current, voltage, heat dissipation). Familiar with electrical concepts for wiring Routing, Schematics. Knowledge of automotive-grade materials and manufacturing processes related to wiring harnesses. Proficient on wiring creation, Able to select circuit IDs Able to calculate circuit gauges. Able to understand multiplexed logic Proficient on CHS, meaning that he knows how to perform the following processes: Schematic Generation following standards in Capital Logic Harness Generation on MOD XC Proficient on Chart and BOM creation Familiar with Deviations Familiar with IDD charting Able to create test cases to validate correct operation of Mechatronic features (DVP&R). Able to engage with different OEMs for wiring requirements Nice to have: Ability to update the global ECU parameters (domains/fragments) using ODX Studio. Ability to convert the supplier provided flash file into DT Ceasar files or SMR files using ODX Studio. Ability to release the ECU parameters, softwares etc., through EDLS Client. Ability to understand the feature needs and accomplish the feature using Flexible Logic Software for global ECUs. Ability to create CHEC parameters using CHEC/CHEC+ Ability to review the Diagnostic Link log files to determine the root cause of the ECU faults and parameter related issues. Knowledge on Diagnose Portal of ECU cdd, cbf etc., Experience with NX Proficient in using DOORS Next and reviewing the component specifications, feature specs, requirements etc.

Job description Role & responsibilities Experience of 2 to 6 years in Wiring Harness design for the automotive industry using UG NX. Should be able to execute: pls share your updated CV/Resume to pradeep_kranth@onwardgroup.com Experience with UG NX in Wiring Harness with 2 to 6 years of experience. Create 3D Designs for low Voltage/High Voltage Wiring Harnesses considering EMC requirements. Must have Wiring harness domain knowledge and knows component selection criteria Create 2D Drawings for Low Voltage/High Voltage Wiring Harness Detecting/Determining of suitable wiring materials, shielding, protections based on surrounding parts/geometry Develop effective routing paths of 3D Model with the help of retainers, channels, grommets, etc.. Design brackets, trays, enclosures, grommets and other components to support wiring harness design and electrical modules packaging. Basic Knowledge of 2D drawing & 3D DMU Automotive Domain experience Good communication skills and ability to work in & contribute as a good team player. pls share your updated CV/Resume to pradeep_kranth@onwardgroup.com

Role & responsibilities 1.Should have strong experience in Automotive Electrical Domain 2.Expertise of Electrical Wiring Harness schematic design, Electric circuit design. 3.Expertise in using CREO Electrical & CREO schematics work benches. 4.Create LV Electrical schematic. 5.Update electrical schematics for Vehicle. 6.Experience of Harness component selection Like connector, terminal, wire, Covers. 7.Experience of using Teamcenter and/or other PDM too.

Job description Role & responsibilities Experience of 2 to 6 years in Wiring Harness design for the automotive industry using UG NX. Should be able to execute: Experience with UG NX in Wiring Harness with 2 to 6 years of experience. Create 3D Designs for low Voltage/High Voltage Wiring Harnesses considering EMC requirements. Must have Wiring harness domain knowledge and knows component selection criteria Create 2D Drawings for Low Voltage/High Voltage Wiring Harness Detecting/Determining of suitable wiring materials, shielding, protections based on surrounding parts/geometry Develop effective routing paths of 3D Model with the help of retainers, channels, grommets, etc.. Design brackets, trays, enclosures, grommets and other components to support wiring harness design and electrical modules packaging. Basic Knowledge of 2D drawing & 3D DMU Automotive Domain experience Good communication skills and ability to work in & contribute as a good team player.

Job Description: We are looking for a skilled Data / Analytics Engineer with hands-on experience in vector databases and search optimization techniques . You will help build scalable, high-performance infrastructure to support AI-powered applications like semantic search , recommendation systems , and RAG pipelines . Key Responsibilities: Optimize vector search algorithms for performance and scalability. Build pipelines to process high-dimensional embeddings (e.g., BERT , CLIP , OpenAI ). Implement ANN indexing techniques like HNSW , IVF , PQ . Integrate vector search with data platforms and APIs . Collaborate with cross-functional teams (data scientists, engineers, product). Monitor and resolve latency , throughput , and scaling issues. Must-Have Skills: Python AWS Vector Databases (e.g., Elasticsearch , FAISS , Pinecone ) Vector Search / Similarity Search ANN Search Algorithms HNSW , IVF , PQ Snowflake / Databricks Embedding Models – BERT , CLIP , OpenAI Kafka / Flink for real-time data pipelines REST APIs , GraphQL , or gRPC for integration Good to Have: Knowledge of semantic caching and hybrid retrieval Experience with distributed systems and high-performance computing Familiarity with RAG (Retrieval-Augmented Generation) workflows Apply Now if You: Enjoy solving performance bottlenecks in AI infrastructure Love working with cutting-edge ML models and search technologies Thrive in collaborative , fast-paced environments

Role & responsibilities Experience of 2 to 6 years in Wiring Harness design for the automotive industry using UG NX. Should be able to execute: Experience with UG NX in Wiring Harness with 2 to 6 years of experience. Create 3D Designs for low Voltage/High Voltage Wiring Harnesses considering EMC requirements. Must have Wiring harness domain knowledge and knows component selection criteria Create 2D Drawings for Low Voltage/High Voltage Wiring Harness Detecting/Determining of suitable wiring materials, shielding, protections based on surrounding parts/geometry Develop effective routing paths of 3D Model with the help of retainers, channels, grommets, etc.. Design brackets, trays, enclosures, grommets and other components to support wiring harness design and electrical modules packaging. Basic Knowledge of 2D drawing & 3D DMU Automotive Domain experience Good communication skills and ability to work in & contribute as a good team player.