[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) # Principal Applied Research Engineer 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[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceSeniorPublished16 Oct 2026Apply before15 Nov 2026Listing views40Application actions2Application 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. Synthesia seeks a Principal Applied Research Engineer to lead the end-to-end technical direction of its offline generative-video model stack. This hands-on principal IC role spans large-scale pre-training, post-training, data quality, distributed training stability, alignment, and production deployment. The engineer will set multi-year technical direction with research leadership while resolving critical cross-team problems and accelerating research-to-product delivery. Success requires demonstrated experience training large multimodal or video models from scratch, applying methods such as RLHF, GRPO, and DPO, and influencing senior technical teams without formal authority. ## 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 5/5IndependentCollaborative AI insightThis is a frontier, principal-level research engineering role requiring direct ownership of large distributed video-model training and post-training systems at substantial data and compute scale. It combines deep technical execution with strategy, cross-functional influence, mentorship, and responsibility for turning research advances into shipped capabilities. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Highly competitive$300,000US market range (per year)$260k–$340k0$374.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer compensation is disclosed. The annual USD figures are AI estimates for a principal-level applied research engineer specializing in large-scale generative video and multimodal models; the yearly cadence is inferred from the full-time senior professional role. ## Core skills Skills and capabilities most closely associated with this opportunity. [Generative video modeling](https://jobicy.com/jobs?search_keywords=Generative%20video%20modeling.md)[Multimodal machine learning](https://jobicy.com/jobs?search_keywords=Multimodal%20machine%20learning.md)[Foundation model training](https://jobicy.com/jobs?search_keywords=Foundation%20model%20training.md)[Distributed training](https://jobicy.com/jobs?search_keywords=Distributed%20training.md)[Deep learning](https://jobicy.com/jobs?search_keywords=Deep%20learning.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[RLHF](https://jobicy.com/jobs?search_keywords=RLHF.md)[GRPO](https://jobicy.com/jobs?search_keywords=GRPO.md)[DPO](https://jobicy.com/jobs?search_keywords=DPO.md)[Model alignment](https://jobicy.com/jobs?search_keywords=Model%20alignment.md)[Data quality](https://jobicy.com/jobs?search_keywords=Data%20quality.md)[Training stability](https://jobicy.com/jobs?search_keywords=Training%20stability.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Research-to-production](https://jobicy.com/jobs?search_keywords=Research-to-production.md)[Technical leadership](https://jobicy.com/jobs?search_keywords=Technical%20leadership.md) Sample interview questionsDescribe a large generative model you trained from scratch and the most difficult training issue you resolved.I would explain the model architecture, dataset scale, compute topology, evaluation criteria, and my direct technical ownership. I would then walk through the failure signals, experiments, root-cause analysis, and the measurable improvement in stability, quality, throughput, or cost. How would you decide whether to address a video-model quality issue during pre-training or through post-training? I would first characterize whether the issue is a capability, data-coverage, controllability, or preference-alignment gap. I would compare the expected durability, data requirements, compute cost, and product impact of each approach, then run targeted experiments while preserving a clear evaluation suite. How have you applied human feedback or preference optimization to a multimodal model? A strong answer should cover feedback collection and quality control, reward or preference-model design, the choice of methods such as DPO, GRPO, or RLHF, and safeguards against reward hacking. It should also explain how offline metrics and human evaluations demonstrated improved user-facing outcomes. How do you set a multi-year technical roadmap while remaining hands-on in a fast-moving research organization? I would define a small number of capability milestones tied to product outcomes, identify the highest-risk technical assumptions, and convert them into near-term experiments and infrastructure investments. I would remain directly involved in pivotal design reviews, debugging, and training runs while making priorities and trade-offs transparent across teams. Give an example of influencing research, engineering, and product partners without formal authority. I would describe aligning stakeholders around a shared problem definition, evidence-based options, and explicit success metrics. I would emphasize concise communication, ownership of follow-through, and how the resulting decision improved execution speed or the quality of a shipped model. 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. ### The opportunity At Synthesia we really care about video generation, especially about human centric avatar video generation. This led us to release models such as [EXPRESS-Video](https://synthesiaresearch.github.io/express-video/), and soon our latest video model – these are the best avatar video models in the world, and we are committed to continuing and double down our efforts in leading that area. Our goal is to get to human centric video models that can generate arbitrary long videos at high resolution with arbitrary actions and events. That means continuously training large generative video models from scratch with the proprietary data pipelines and compute infrastructure to support it at scale. We are looking for a technical leader who owns the full stack end-to-end, someone who bridges pre-training and post-training, sets long-term direction alongside research leadership, and is personally present at the hardest parts of the work. If building foundation model capability from the ground up at a company genuinely committed to leading the field sounds like the right next challenge, this role was written for you. ### About the role Synthesia’s video generation capability is core to everything we ship. It involves roughly 15 people working daily across pre-training and post-training stages, and the complexity of coordinating across those stages, at the scale of compute and data we now operate, requires a different kind of technical leadership. We’re looking for a Principal Research Engineer (L7) to own the full technical stack for offline video generation. This is a senior individual contributor position with outsized scope and influence. You’ll partner directly with research leadership and team leads to define long-term strategy, resolve the hardest cross-cutting technical problems, and raise the bar for how quickly research reaches product. The person we’re looking for has trained large generative models from scratch, not supervised it from a distance, but done it, debugged it, and shipped it. More than that, they’re driven by a genuine ambition to push what’s possible in video generation, and they care deeply about seeing that work land in product and reach users. What you’ll do * Own the end-to-end technical direction for offline video generation, spanning pre-training and post-training, resolving the artificial boundary between those two stages in service of shipping better models faster. * Partner with research leadership and team leads to define a unified long-term roadmap, broken into achievable objectives, and drive execution against it. * Identify the most critical technical gaps across the video generation pipeline and jump in to unblock them, whether that means architectural decisions, training stability, post-training alignment, or cross-team coordination. * Increase the velocity at which research ships to product: accelerate problem-solving, improve research-to-production handoffs, and increase visibility of research output in partnership with PMs. * Coach and elevate more junior researchers and engineers toward senior technical thinking and execution. * Help shape team structure and refine processes to enable high-velocity, cohesive execution across research. You’ll thrive in this role if you have * A proven track record training large-scale video generation models from scratch, across multiple nodes, at the scale of millions of hours of data. * Deep experience with post-training techniques at scale: RLHF, GRPO, DPO, and the judgment to know when and how to apply them. * A proven track record data quality and you are not reluctant to question it. * The ability to think strategically about multi-year research direction and execute hands-on at the frontier of what the team is building. * Strong cross-functional influence: you shape how teams work together without needing positional authority to do it. * A leadership style grounded in technical involvement. You lead by example, inspire through craft, and communicate with clarity. * Genuine hunger to unlock new capabilities and an obsession with shipping. You’re not satisfied by research that stays in a notebook. You want it in the hands of users. Particularly relevant experience * Having owned a major model generation or capability jump end-to-end, from training runs through to product deployment. * Working across both pre-training and post-training stages on the same model family, with direct accountability for the outcomes of both. * Experience operating at scale: large distributed training runs, significant compute budgets, and multi-million hour data pipelines. * Applying alignment and fine-tuning techniques in a video or multimodal context, not just text. * Experience with human feedback pipelines applied to generative video or audio. * Leading or significantly influencing the technical direction of a research team while remaining hands-on. Dealbreakers We will not be a good fit if you prefer to lead without staying technically involved, or if clear and direct communication across research and product isn’t one of your strengths. This role requires presence at the frontier of the work, not above it. And if shipping doesn’t excite you as much as the research itself, this probably isn’t the right role. Show more [Apply now >](https://jobicy.com/jobs/155435-principal-applied-research-engineer.md) > Annual salary information is not provided for this position. 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