[![Image]() Meet Jobicy Copilot — free AI autofill for job applications + remote job alerts ›](#) [All remote jobs](https://jobicy.com/jobs.md)Open role[![Synthesia logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2026/06/c69aad11-221.webp)](https://jobicy.com/company/synthesia.md)Remote opportunity at[Synthesia](https://jobicy.com/company/synthesia.md) # Research Engineer in Data 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)Share10 Sep 2026Published29Listing views3Application actions10 Oct 2026Apply before Opportunity details ## About this role. AI SummarySynthesia is seeking a Research Engineer in Data to build and improve the data layer supporting generative AI research and model training. The role combines applied machine learning, data engineering, and ML infrastructure, with a focus on sourcing, curating, annotating, processing, and evaluating large-scale video and audio datasets. The engineer will collaborate closely with model-training teams to translate research needs into higher-quality datasets and features that improve model performance. Strong Python engineering practices are essential, while experience with workflow orchestration and large-scale processing systems is advantageous. The position is full time and supports hybrid work from select European hubs or remote work within Europe. ## 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 4/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a technically demanding role involving data-centric ML for generative video and audio at million-hour scale. Success requires strong judgment across data quality, scalable pipeline design, experimentation, and collaboration with research and model-training stakeholders. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$170,000US market range$145k–$200k0$220k AI insightNo numerical salary was disclosed, so these are estimated US-market annual base-salary figures in USD for a senior-level research/data engineer working on large-scale generative-AI data systems. Actual compensation may vary materially by country, level, cash-versus-equity mix, and location; the posting also mentions stock options and a bonus without amounts. ## Core skills Skills and capabilities most closely associated with this opportunity. [Applied Machine Learning](https://jobicy.com/jobs?search_keywords=Applied%20Machine%20Learning.md)[Data Engineering](https://jobicy.com/jobs?search_keywords=Data%20Engineering.md)[Generative AI](https://jobicy.com/jobs?search_keywords=Generative%20AI.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Data Curation](https://jobicy.com/jobs?search_keywords=Data%20Curation.md)[Data Labeling](https://jobicy.com/jobs?search_keywords=Data%20Labeling.md)[Dataset Evaluation](https://jobicy.com/jobs?search_keywords=Dataset%20Evaluation.md)[Video Data](https://jobicy.com/jobs?search_keywords=Video%20Data.md)[Audio Data](https://jobicy.com/jobs?search_keywords=Audio%20Data.md)[Workflow Orchestration](https://jobicy.com/jobs?search_keywords=Workflow%20Orchestration.md) Sample interview questionsDescribe a project where improving data quality produced a measurable improvement in model performance.I would explain the baseline model metric, identify the data issue through slice analysis or error review, and describe the intervention, such as relabeling, deduplication, rebalancing, or filtering. I would then quantify the improvement using a controlled evaluation and explain how I operationalized the quality checks to prevent regression. How would you design a pipeline for processing and curating millions of hours of video and audio data? I would use modular, versioned stages for ingestion, validation, metadata extraction, deduplication, feature generation, annotation, and publishing. The system should use distributed compute, idempotent jobs, data lineage, observability, and quality gates, with reproducible dataset versions that model teams can reliably consume. How do you decide which new annotations or features are worth adding to a training dataset? I would start with model-team hypotheses and error slices, then assess whether a proposed annotation is predictive, reliable to generate, and actionable in training or evaluation. I would run a small pilot, measure annotation agreement and model lift against a baseline, and only scale features that show meaningful value relative to cost and operational complexity. What practices do you use to ensure Python data-processing code remains maintainable and trustworthy? I use clear interfaces, typed and documented code where appropriate, unit and integration tests, deterministic transformations, structured logging, and code review. For production pipelines, I also add data contracts, schema validation, monitoring for volume and distribution shifts, and reproducible environments. How would you collaborate with researchers when their data requirements are incomplete or change frequently? I would turn broad requests into a short written specification covering the target model behavior, required slices, dataset definitions, quality thresholds, and evaluation metrics. I would deliver an early sample or prototype, review findings together, and iterate through versioned datasets so changes remain traceable and do not disrupt existing experiments. 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 The Data team manages the complete lifecycle of data for researchers – from sourcing and large-scale processing to delivering datasets that power our models. Data sits at the heart of our Research efforts and enables all other teams. As part of the Data team, you’ll work with over a million hours of video and audio data. This role exists at the intersection of applied research, data engineering, and ML infrastructure rather than being a traditional research position. You’ll build the world’s best human-centric data lake by collaborating closely with our model training teams. By understanding their requirements, you’ll extract new features and annotations that elevate our datasets. You should be passionate about enhancing model performance through high-quality, accurate datasets. Our infrastructure and pipelines are in great shape, and this role provides room to not only enhance them but also influence the team’s longer-term strategy. ### What we’re looking for: * A strong background in data-centric, applied Machine Learning, with hands-on experience improving model performance through data quality, curation, labeling, and evaluation rather than model architecture alone * Experience working on the data layer of Generative AI products, particularly involving images, video, or audio * Excellent Python skills, with a strong focus on writing clean, maintainable, and well-tested code * Nice to have if you have experience designing, building, and operating workflow orchestration systems and large-scale data processing pipelines ### 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.](https://www.notion.so/How-we-work-at-Synthesia-f794caa72f8446efb6be22b551ce0fa2?pvs=21) ### 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](https://www.synthesia.io/case-studies) and what [1,200+ people say on G2](https://www.g2.com/products/synthesia/reviews#reviews). ### 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.](https://www.synthesia.io/research) ### 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](https://www.synthesia.io/ethics). ### 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 #LI-MD1 Show more [Apply now >](https://jobicy.com/jobs/152972-research-engineer-in-data.md) > Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. 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