Senior Research Engineer - Data

SLAMcore

Deutschland

Vor Ort

EUR 60.000 - 90.000

Vollzeit

14 Tage+

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Benefits dieser Stelle

Competitive compensation
Hybrid work setting
25 days annual leave
Great company culture

Zusammenfassung

SLAMcore is seeking a Data-focused role within their innovative team to build a top-tier human-centric data lake. The ideal candidate will have a strong background in applied machine learning, work with data for generative AI, and possess excellent Python skills for developing workflows. This position offers a hybrid work environment across Europe and includes competitive compensation, 25 days of annual leave, and a vibrant company culture.

Qualifikationen

  • Strong background in data-centric applied Machine Learning.
  • Experience with data layer of Generative AI products.
  • Excellent Python skills, focusing on clean and maintainable code.
  • Hands-on experience with workflow orchestration and data processing.

Aufgaben

  • Build the world’s best human-centric data lake.
  • Collaborate with model training teams for feature extraction.
  • Enhance model performance through high-quality datasets.
  • Influence the team's longer-term strategy regarding data management.

Kenntnisse

Data-centric applied Machine Learning
Python programming
Workflow orchestration systems
Large-scale data processing pipelines

Jobbeschreibung

Company Overview

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.

  • Hands‑on experience designing, building, and operating workflow orchestration systems and large‑scale data processing pipelines.

Benefits
  • 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.

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