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Corporate Communications Manager Jobs

2,972 active opportunities · Updated for October 2026

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Explore current corporate communications manager jobs. Use filters to narrow by work mode, employment type, experience and date posted.

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Cohere
📍 Toronto• Full-time• Remote• C$250K – C$535K/yr
4mo ago

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: As a Senior Member of Technical Staff specializing in web data for pre-training, you will play a pivotal role in developing the large scale web data pipeline that underpins Cohere’s advanced language models. In this role, you will work extensively with large-scale web corpora, transforming raw, noisy internet data into high-quality training data for pretraining. You will own key components of the data pipeline, including extraction, parsing, deduplication, and filtering. You will also analyze the composition and quality of web data, study its impact on downstream model performance, and collaborate closely with the broader data and evaluation teams to iterate on the training corpus. Your work will be essential to Cohere’s mission of delivering efficient and reliable language understanding and generation capabilities, driving innovation in natural language processing. If you are passionate about transforming data into the foundation of AI systems, this role offers a unique opportunity to make a meaningful impact. Please Note: We have offices in London, Paris, Toronto, San Francisco and New York but also embrace bein

REMOTEpythonairecruitment
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Hark
📍 San Jose• $180K – $450K/yr
6mo ago

About Hark Hark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and persistent memory. We're pairing that intelligence with next-generation hardware to create a universal interface between humans and machines. While today's AI largely operates through chat boxes and decade-old devices, Hark is focused on what comes next: agentic systems that interact naturally with people and the real world. To get there, we're developing multimodal models and next-generation AI hardware together - designed from the ground up as a single, unified interface for a new era of intelligent systems. About the Role The Omni team at Hark is building the next generation of AI experiences beyond text, enabling models to understand and generate content across multiple modalities, including text, and vision. Our goal is to create seamless, real-time multimodal intelligence that powers intuitive and immersive user experiences. As part of the Omni team, you will help drive the development of text, video, and multimodal models. This includes working across the full stack—from data and modeling to training, serving, and product integration. You will contribute to both pretraining and posttraining efforts while collaborating closely with product teams to push the boundaries of model capability and deliver exceptional end-to-end user experiences. Responsibilities Drive mid-training and post-training research for multimodal foundation models — continued/mid-training on large-scale interleaved vision-text and video-audio corpora, supervised fine-tuning, and reinforcement learning to advance image, video, and video-audio understanding. Own the RL stack for multimodal post-training: reward modeling, preference optimization and RLVR-style verifiable rewards, policy optimization (PPO / GRPO / DPO and variants), rollout and sampling strategies, and mitigation o

machine learningartificial intelligenceai
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