This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role offers the opportunity to lead cutting-edge AI and machine learning initiatives, working closely with a highly-skilled team of engineers and researchers. You will design, train, and optimize ML models, translating advanced research into scalable, production-ready systems that solve real-world problems. The position involves building robust ML pipelines, running iterative experiments, and collaborating cross-functionally to integrate AI technologies into key products. This is a remote-friendly, high-impact role where innovation, experimentation, and technical rigor are highly valued. You will have the chance to influence AI strategy, contribute to state-of-the-art research, and deliver solutions that directly benefit customers. - Design, develop, train, and evaluate machine learning models across research and applied AI initiatives. - Build and maintain robust ML pipelines for data ingestion, feature engineering, model training, and evaluation. - Run iterative experiments, test hypotheses, and refine models to improve performance and accuracy. - Collaborate with researchers, engineers, and stakeholders to translate academic advances into production-ready solutions. - Optimize model performance through fine-tuning, hyperparameter search, and architecture experimentation. - Document experimental results, track findings, and share learnings with the broader team. - Stay current with AI/ML research trends and identify opportunities to apply new techniques to product solutions. - Participate in stand-by, on-call, or off-hours duties during critical research or deployment milestones as needed. Qualifications - Bachelor’s degree in Computer Science, Mathematics, Statistics, or a related field (Master’s or PhD preferred). - 5+ years of experience in industry or research, with deep hands-on expertise training and evaluating ML models, including language models. - Proficiency in Python and ML frameworks such as PyTorch or TensorFlow. - Experience with MLOps tooling and infrastructure (e.g., MLflow, Weights & Biases, Kubeflow, or similar). - Solid understanding of modern NLP, computer vision, and/or reinforcement learning techniques. - Strong analytical and problem-solving skills with the ability to iterate quickly without sacrificing rigor. - Excellent communication skills, capable of presenting experimental results to technical and non-technical stakeholders. - Ability to work independently and collaboratively in a fast-paced, remote-first environment. Benefits - Competitive salary range: $230,000–$275,000, dependent on experience and location. - Comprehensive health, dental, and vision insurance. - Short-term disability and life insurance coverage. - Paid holidays and generous paid time off. - 401(k) plan and company equity. - Fertility treatment benefits. - Eligibility for a discretionary company-wide bonus. - Remote-first work environment with flexible work arrangements. Company Description

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a Temporary Research Scientist to support the healthcare AI project in developing a trustworthy healthcare large language model. - The role involves hands-on LLM fine-tuning (including instruction tuning, PEFT/LoRA, domain adaptation, and alignment) on clinical datasets. - Designing rigorous evaluation protocols. - Contributing substantially to publications by drafting key manuscript sections and preparing results for top-tier venues. Qualifications - Candidates must hold a PhD in a relevant field. - Demonstrate strong expertise in LLM fine-tuning with frameworks like Hugging Face Transformers and PyTorch. - Have a proven publication record with significant contributions in healthcare AI, NLP, or trustworthy LLM research in premier conferences/journals. Requirements - Pay rate: $25 per hour. - This is a fully remote position, working 10 hours per week. Benefits - WPI is an Equal Opportunity Employer. - All qualified candidates will receive consideration for employment without regard to race, color, age, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability. - It seeks individuals from all backgrounds and experiences who will contribute to a culture of creativity, collaboration, inclusion, problem solving, innovation, high performance, and change making. - It is committed to maintaining a campus environment free of harassment and discrimination.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Generative AI Specialist to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Be Doing - Evaluation: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. - Annotation Labeling: Labeling elements of a piece of content rather than the content as a whole. - Classification: Assigning predefined categories or labels to items. - Content Quality: Evaluating the perceived quality and/or appropriateness of content. - Content Understanding: Generating labels to advance understanding of a concept, trend, etc. - Data Augmentation: Creation of additional training data for machine learning models by applying transformations to the original data. - Grading: Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. - Identification Labeling: Labeling model outputs to identify if a piece of content is or isn't something. - Preference Ranking: Ordering or ranking items based on a set of preferences or criteria. - Prompt Generation: Creating prompts or questions that will be used to generate responses from a language model or other AI system. - Relevance Evaluation: Projects that evaluate the relevance of content based on a relevancy scale. - Response Generation: Generating responses to prompts or questions using a language model or other AI system. - Response Rewrite: Rewriting existing text while preserving the original meaning. - Response Summarization: Producing concise summaries of longer pieces of text or data. - Similarity Evaluation: Projects where content is compared in order to drive a determination. - Transcription: Converting spoken language or audio content into written text. - Translation: Converting text or spoken language from one language to another. - Data Collection: Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. Qualifications - A Bachelor’s degree or higher in a humanities specialization is required. - Advanced degrees are strongly preferred (Master’s or PhD). - Professional or Expert level proficiency (C1/C2) in English and Italian. Requirements - Applicants must be legally authorized to work in the United States at the time of hire. - Innodata is unable to provide visa sponsorship now or in the future for this position. Benefits - Salary rates at Innodata vary depending on a wide array of factors, which may include but are not limited to the role, skill set, educational background and geographic location.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We're seeking experienced AI infrastructure Engineers to design and implement robust, scalable pipelines for massive data workloads. Join Tether’s applied research team, where you’ll contribute to high-impact projects that run across thousands of GPUs and drive cutting-edge video generation foundation development. Responsibilities - Build and scale high-throughput data infrastructure optimized for video and multimodal content processing across large GPU clusters (e.g., H100/H200). - Design core preprocessing algorithms for video, audio, text, and image modalities, enabling efficient extraction, synchronization, and normalization of temporal data. - Build automated acquisition pipelines for sourcing large-scale video datasets, handling diverse formats, frame rates, annotations, and embedded audio. - Architect robust systems for scalable evaluation and annotation, including prompt-based scoring, perceptual metrics, caption generation, and retrieval-based diagnostics. - Collaborate with model researchers to co-design video model architectures (e.g. DiTs, VAEs, spatio-temporal transformers) and training schedules across pretraining and fine-tuning stages. - Optimize distributed data loading and pipeline throughput for training at scale, ensuring robustness across model variants and modality combinations. - Manage infrastructure to support experiment tracking, model versioning, and cross-team deployment workflows, integrating with production and research platforms. - Support backend engineering across research, product, and creative teams to ensure seamless integration of data and model workflows from prototyping to inference. Qualifications - Proficient in Python with strong programming skills across backend, infrastructure, and data tooling domains. - Strong software engineering experience, including 2+ years working with petabyte-scale data pipelines and systems across thousands of GPUs. - Proven ability to architect and maintain large-scale distributed systems for data processing and delivery. - Deep expertise in orchestration frameworks such as Kubernetes and SLURM with hands-on experience deploying and managing high-throughput workloads. Preferred Qualifications - Practical experience on building pipelines and infrastructure with visual and multimodal datasets, including image/video pipelines. - Experience in building video foundation infrastructure pipelines and workflows with collaboration of LLM and/or video foundation research and engineering teams is a strong advantage. Important information for candidates - Apply only through our official channels. - We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/ - Verify the recruiter’s identity. All our recruiters have verified LinkedIn profiles. - Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. - Double-check email addresses. All communication from us will come from emails ending in @tether.to or @tether.io. - We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam.