[All remote jobs](https://jobicy.com/jobs.md)[![Synthesia logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2026/06/c69aad11-221.webp)](https://jobicy.com/company/synthesia.md)[Synthesia](https://jobicy.com/company/synthesia.md) # Staff Research Engineer – Multimodal Generative Modelling Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/synthesia.md)ShareRemote from[Europe](https://jobicy.com/job-region/europe.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished16 Oct 2026Apply before15 Nov 2026Listing views47Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. This Staff Research Engineer role leads research and production delivery for interactive multimodal generative models, with an emphasis on voice-to-voice systems. The position spans architecture design, large-scale training, post-training, evaluation, and runtime optimization for low-latency conversational experiences. It requires deep expertise in generative modeling, transformer-based systems, PyTorch, and audio, speech, or video sequence modeling. The engineer will shape the research roadmap and collaborate closely with voice, video, product, and senior R&D stakeholders. Success depends on shipping robust multimodal models at meaningful product scale while advancing natural, emotionally expressive interaction. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a staff-level research engineering role requiring original technical direction as well as end-to-end production delivery of advanced multimodal models. The work combines frontier research, distributed training, latency-sensitive systems engineering, and cross-functional leadership. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$230,000US market range (per year)$180k–$300k0$330k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer compensation was disclosed. The yearly cadence is inferred from the full-time staff-level research engineering position; the figures shown are estimated annual USD market compensation for a comparable US-market role, not employer-provided pay. ## Core skills Skills and capabilities most closely associated with this opportunity. [Multimodal generative modeling](https://jobicy.com/jobs?search_keywords=Multimodal%20generative%20modeling.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[Distributed training](https://jobicy.com/jobs?search_keywords=Distributed%20training.md)[Large language models](https://jobicy.com/jobs?search_keywords=Large%20language%20models.md)[Speech synthesis](https://jobicy.com/jobs?search_keywords=Speech%20synthesis.md)[Audio modeling](https://jobicy.com/jobs?search_keywords=Audio%20modeling.md)[Video generation](https://jobicy.com/jobs?search_keywords=Video%20generation.md)[Transformer architectures](https://jobicy.com/jobs?search_keywords=Transformer%20architectures.md)[Diffusion models](https://jobicy.com/jobs?search_keywords=Diffusion%20models.md)[Neural codecs](https://jobicy.com/jobs?search_keywords=Neural%20codecs.md)[Flow matching](https://jobicy.com/jobs?search_keywords=Flow%20matching.md)[DPO](https://jobicy.com/jobs?search_keywords=DPO.md)[Model fine-tuning](https://jobicy.com/jobs?search_keywords=Model%20fine-tuning.md)[Model distillation](https://jobicy.com/jobs?search_keywords=Model%20distillation.md)[Production ML systems](https://jobicy.com/jobs?search_keywords=Production%20ML%20systems.md) Sample interview questionsHow would you design a voice-to-voice model that can respond naturally while supporting interruptions and backchanneling?I would begin with a streaming architecture that separates fast incremental perception from generation while sharing a time-aligned conversational state. The model should represent acoustic, linguistic, and turn-taking signals jointly, then use explicit policies for interruption, overlap, and backchannel decisions. I would evaluate it with both offline quality measures and interactive latency, interruption-recovery, and perceived-naturalness tests. Describe how you would take a promising multimodal research prototype to production. I would first establish a reproducible data, training, and evaluation pipeline with clear quality and latency baselines. After validating the architecture through ablations, I would optimize inference through batching, quantization or compilation where appropriate, streaming execution, and robust observability. A staged release with user feedback, quality monitoring, and rollback criteria would guide further post-training and iteration. What trade-offs matter most when optimizing a real-time conversational generative model? The central trade-off is between response quality and end-to-end latency, but interruption behavior, stability, cost, and consistency are also critical. I would measure time to first response, token or audio chunk cadence, barge-in recovery, and degradation under load alongside subjective quality. The right solution often uses incremental generation and carefully bounded context rather than maximizing offline benchmark scores alone. How would you evaluate whether a new model architecture improves natural conversation rather than only speech quality? I would combine objective and human evaluation. Objective measures would include response latency, overlap handling, turn-transition timing, interruption recovery, semantic relevance, and prosodic consistency. Human studies should assess perceived responsiveness, emotional appropriateness, conversational flow, and whether users feel they are interacting with a natural partner. How do you approach cross-functional technical leadership in a research-heavy organization? I align teams around an explicit product outcome, technical hypothesis, measurable milestones, and known risks. I communicate architectural decisions and experimental evidence clearly so research, infrastructure, and product partners can contribute early. I also maintain ownership of the end-to-end path, ensuring research insights become reliable systems rather than isolated prototypes. Opportunity details ## About this role. Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US. As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations. Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia’s VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow. ### About the role Synthesia’s long-term vision is to build the best human-interactive models: systems that don’t just talk to people, but perceive and respond to them, reacting to a user’s actions and emotions, not only their words. Today over 60,000 businesses rely on our platform, and the next leap in what we can offer them depends on models that combine text, audio, and video into a single real-time interactive experience. As a Staff Research Engineer, you’ll join the Voice team within our 40+ person R&D department, but your scope will extend well beyond voice. You’ll help define and drive that broader vision across teams, proposing ambitious research directions and taking direct ownership of the design and implementation of its most critical components. You’ll work directly with our voice lead and collaborate tightly with our video teams and other senior members of the org. Concretely, you’ll work on voice to voice models that produce text and voice simultaneously. Models that can reason, interrupt the user with back channeling and talk. Models that feel like you are having a natural conversation with, without the feeling of turn taking that is dominant in current speech to speech models. Your role would be to partner in defining a roadmap, implementing it and shipping the outcomes to product. What you’ll do * Shape our roadmap to create new model capabilities and unlock new functionality for our customer base, on both short and long time horizons. * Propose novel multi-modal system architectures (especially text and voice). * Develop and evaluate streaming and conversational systems for low-latency, interactive voice-video synthesis. * Design solutions that reinforce emotional expressiveness and natural interaction. * Implement and bring designs to life, from pretraining through post-training. * Integrate and test novel architectures (neural codecs, diffusion, flow-matching) to enhance realism and responsiveness. * Define new evaluation metrics for conversational systems, including latency-aware and interaction-based measurements. * Track the latest research in audio-visual diffusion, autoregressive models, neural codecs, and multimodal LLMs. * Curate new datasets to complement existing data. * Lead post-training initiatives like DPO, fine-tuning, and distillation to bring models to shipping quality. * Ship models to production with optimised runtime to serve customers, and address their feedback thereafter. ### You’ll thrive in this role if you have * The ability to bring novel ideas and designs that advance the field of interactive multimodal systems. * Strong understanding of generative modelling, ideally applied to sequential or multimodal data. * Hands-on experience with large language models or similar transformer-based architectures. * High proficiency in PyTorch, including distributed training and model optimization. * A solid grasp of time-series modeling and tokenization, preferably in the context of audio, speech, or video. * A demonstrated ability to prototype quickly, test hypotheses, and iterate efficiently. * Proven experience training deep learning models end-to-end, from data preparation through evaluation. * Strong general software engineering skills, enabling contributions to a large, shared research infrastructure. ### Particularly relevant experience * Having shipped a generative model into a live product used at meaningful scale, not just published or prototyped it. * Working on conversational or interactive systems where latency, responsiveness, and user experience were first-class constraints, not afterthoughts. * Working on LLMs with large scale trainings leadings to models with decent reasoning capabilities * Owning a research problem end to end: from architecture proposal through pretraining, post-training, and production deployment. * Collaborating across modalities or teams (e.g. audio and video, or research and product) to ship a unified system. ### Bonus points for * Experience with real-time or streaming architectures. * Familiarity with state-of-the-art architectures in audio and speech generation, such as diffusion models, neural codecs, flow-matching models, or autoregressive decoders. * Excellence in one or more of the following modalities: voice, text, video. * Evidence of original research contributions, such as publications or open-source work at top-tier venues (e.g. NeurIPS, CVPR, ICML, ICLR, Interspeech). Show more [Apply now >](https://jobicy.com/jobs/155440-staff-research-engineer-multimodal-generative-modelling.md) > Annual salary information is not provided for this position. 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