Data Engineer, Multimodal Systems

Zyphra Technologies Inc

$120K — $150K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5-7 years of relevant experience in data engineering or related fields
  • Strong implementation and prototyping skills
  • Ability to rapidly turn concepts into experiments
  • Collaborative mindset in fast-paced research environments
  • Eagerness to learn and apply new technologies

Responsibilities

  • Collect large-scale datasets across multiple modalities
  • Design and optimize parallelized data processing pipelines
  • Conduct rigorous experimental ablations to assess data improvements
  • Collaborate with cross-functional teams to refine data practices
  • Implement new ideas and technologies rapidly

Benefits

  • Comprehensive medical, dental, vision, and FSA plans
  • Competitive compensation and 401(k)
  • Relocation and immigration support available
  • On-site meals prepared by a culinary team
  • Collaborative, high-energy environment with social events
Full Job Description
Zyphra is an artificial intelligence company based in San Francisco, California.

The Role:

As a Data Engineer - Multimodal Systems, you will be a core contributor to creating, collecting, and improving Zyphra's datasets and data pipelines across a variety of modalities. Your work will intersect with almost every team at Zyphra. You will be involved in collecting large-scale datasets and implementing and optimizing highly parallel data pipelines.

You'll work across:
  • Large-scale data collection across a variety of modalities (text, audio, image)
  • Designing and working with highly efficient, parallelized data processing pipelines across modalities
  • Designing and running rigorous experimental ablations to demonstrate the impact of new data improvements


Requirements:
  • Strong implementation and prototyping ability
  • Can take an idea from conception to experimentation quickly
  • The ability to work well with others in a high-paced research setting
  • Can rapidly learn new fields and are excited to implement new ideas
  • Excellent communication and collaboration skills, and can work effectively on both research and engineering implementation at scale.


Bonus Skill Set:
  • Experience collecting, handling, and processing large datasets
  • Experience with parallel Python programming frameworks such as Dask
  • Understanding of the state-of-the-art in dataset curation across modalities
  • A generally meticulous nature and a strong interest in actually looking at data and sanity checking things
  • Strong grasp of proper experimental methodology for running rigorous ablations and other hypothesis testing
  • Understanding of and interest in large-scale, highly parallel data processing pipelines.
  • Proficiency with PyTorch and Python.
  • Experience contributing to large pre-existing codebases and rapidly getting up to speed.
  • Previously published machine learning research in well-respected venues.
  • Postgraduate degree in a scientific subject (Computer Science, EE/EECS, Mathematics, Physics, Machine Learning)


Why Work at Zyphra:
  • Our research methodology is grounded in methodical, step-by-step approaches to ambitious goals. Both deep research and engineering excellence are equally valued
  • We strongly value new and crazy ideas and are very willing to bet big on new ideas
  • We move as quickly as we can; we aim to minimize the bar to impact as low as possible
  • We all enjoy what we do and love discussing AI


Benefits and Perks:
  • Comprehensive medical, dental, vision, and FSA plans
  • Competitive compensation and 401(k)
  • Relocation and immigration support on a case-by-case basis
  • On-site meals prepared by a dedicated culinary team; Thursday Happy Hours
  • In-person team in San Francisco, CA, with a collaborative, high-energy environment

We strongly look for velocity over position. If you love data and are enthusiastic about learning each aspect as needed to drive our models forward. This is the job for you. Apply today!

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