Senior Research Engineer - Video Post Training at Synthesia

Reims, Grand Est, France -

Full Time

Start Date

Immediate

Expiry Date

25 Dec, 25

Salary

0.0

Posted On

26 Sep, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Computer Vision, Computer Science, Post-Training Techniques, Generative Models, Diffusion, GAN, Python, Machine Learning, Deep Learning, SDLC Tools, Git, CI/CD

Industry

Software Development

Description

Welcome to the video first world From your everyday PowerPoint presentations to Hollywood movies, AI will transform the way we create and consume content. Today, people want to watch and listen, not read — both at home and at work. If you’re reading this and nodding, check out our brand video. Despite the clear preference for video, communication and knowledge sharing in the business environment are still dominated by text, largely because high-quality video production remains complex and challenging to scale—until now…. Meet Synthesia We're on a mission to make video easy for everyone. Born in an AI lab, our AI video communications platform simplifies the entire video production process, making it easy for everyone, regardless of skill level, to create, collaborate, and share high-quality videos. Whether it's for delivering essential training to employees and customers or marketing products and services, Synthesia enables large organizations to communicate and share knowledge through video quickly and efficiently. We’re trusted by leading brands such as Heineken, Zoom, Xerox, McDonald’s and more. Read stories from happy customers and what 1,200+ people say on G2. In February 2024, G2 named us as the fastest growing company in the world. Today, we're at a $2.1bn valuation and we recently raised our Series D. This brings our total funding to over $330M from top-tier investors, including Accel, Nvidia, Kleiner Perkins, Google and top founders and operators including Stripe, Datadog, Miro, Webflow, and Facebook. What you'll do at Synthesia: As a Research Engineer you will join a team of 40+ Researchers and Engineers within the R&D Department working on cutting edge challenges in the Generative AI space, with a focus on creating highly realistic, emotional and life-like Synthetic humans through text-to-video. Within the team you’ll have the opportunity to work on the applied side of our research efforts and directly impact our solutions that are used worldwide by over 55,000 businesses. If you are an expert in post-training Diffusion models for generative AI, this is your chance. This is an opportunity to work for a company that is impacting businesses at a rapid pace across the globe. In this position, you’ll join our Post-Training Team that works on generative video creation. You will make it possible to apply our foundational models to production use cases. This includes projects such as: Apply DPO (direct preference optimisation) to pre-trained models. Adapt models to extend their capabilities, for instance, by changing conditioning inputs. Build solutions for dubbing and evaluate the quality of lip-sync. Implement post-training optimization techniques, such as quantization, pruning and distillation, to improve the efficiency of diffusion models used in avatar generation. Analyze and address challenges related to model performance, ensuring high-quality output in avatar rendering. Stay updated with the latest research and advancements in diffusion models, adversarial networks and post-training optimization methods. What we're looking for: You have a background in Computer Vision / Computer Science and 3+ years of industry experience. You have knowledge of recent advancements in post-training techniques. (for instance distillation, adversarial networks and efficient attention) You have worked with generative models for images and/or videos (Diffusion/GAN) preferably in the avatar domain. You are interested in doing research, trying new things and pushing the boundaries, going beyond what’s already known. You have experience in using most modern frameworks for machine learning and deep learning. You have great coding skills in Python and you care about writing clean code. You have experience with SDLC tools (Git), preferably CI/CD Why join us? We’re living the golden age of AI. The next decade will yield the next iconic companies, and we dare to say we have what it takes to become one. Here’s why, Our culture At Synthesia we’re passionate about building, not talking, planning or politicising. We strive to hire the smartest, kindest and most unrelenting people and let them do their best work without distractions. Our work principles serve as our charter for how we make decisions, give feedback and structure our work to empower everyone to go as fast as possible. You can find out more about these principles here. Serving 50,000+ customers (and 50% of the Fortune 500) We’re trusted by leading brands such as Heineken, Zoom, Xerox, McDonald’s and more. Read stories from happy customers and what 1,200+ people say on G2. Proprietary AI technology Since 2017, we’ve been pioneering advancements in Generative AI. Our AI technology is built in-house, by a team of world-class AI researchers and engineers. Learn more about our AI Research Lab and the team behind. AI Safety, Ethics and Security AI safety, ethics, and security are fundamental to our mission. While the full scope of Artificial Intelligence's impact on our society is still unfolding, our position is clear: People first. Always. Learn more about our commitments to AI Ethics, Safety & Security. The good stuff... Competitive compensation (salary + stock options + bonus) Hybrid work setting with an office in London, Amsterdam, Zurich, Munich, or remote in Europe. 25 days of annual leave + public holidays Great company culture with the option to join regular planning and socials at our hubs + other benefits depending on your location You can see more about Who we are and How we work here: https://www.synthesia.io/careers LI-MD1

Responsibilities

As a Research Engineer, you will work on creating highly realistic synthetic humans through text-to-video and apply foundational models to production use cases. You will also implement post-training optimization techniques to improve the efficiency of diffusion models used in avatar generation.