Staff Applied Scientist, Generative AI

Key Skills

CNNs, Data pipelines, Deep Learning, Distributed systems, Fairness learning, Framework, GANs, GraphQL, HuggingFace, Liferay, MLflow, mpi, Natural Language Processing (NLP), Neural Networks, OpenCV, PyTorch, TensorFlow, Transformer

Job Description

Who we are

At Gretel, we’re building the first developer platform for synthetic data. Gretel’s APIs automatically fine-tune AI models to generate synthetic data on demand while protecting privacy and maintaining the utility and accuracy of the original data. Our synthetic data platform solves the data bottleneck problem for developers, data scientists, and AI/ML researchers across multiple modalities including tabular, time-series, relational, language and image. We're looking for talented Applied Scientists to join our mission!

The impact you’ll have

As an Applied Scientist, you will be strengthening the Gretel team with your expertise in generative modeling, working on ways to create high quality synthetic data to safely share without privacy constraints while enabling model training performance. You'll be building cutting edge deep learning models to unlock customer data via generative AI.

With over 75k developers using Gretel’s platform, hundreds of thousands of models trained, and hundreds of billions of records trained, the impact you can have on the synthetic data industry is massive, and there is so much more to do!

You'll work in the following areas:

• Come up with novel algorithmic and systems solutions to pursue in the advancement of building foundational models.

• Be involved in end-to-end development, exploring new applications and techniques, build distributed systems, study privacy first generative models, and launch production systems.

• Train Models from scratch to improve the performance of low resource data by developing systems, data processing pipelines, and incorporating feedback from our customers.

Requirements

• MS. or PhD in Computer Science, related technical field, or equivalent practical experience.

• 5+ years of industry experience in two or more of the following areas:

• NLP, Deep learning, Language Modeling, Generative modeling

• Strong coding skills and deep experience working with ML frameworks such as TensorFlow, HuggingFace, PyTorch, OpenCV, Fairlearn, or MLflow

• Deep experience with ML techniques such as Transformers, LSTM, GANs, CNNs, Graph Neural Networks, or other deep gradient based methods

• Strong communication skills -– you are able to clearly express ideas both verbally and in written form. We’re a distributed team so we’re extra mindful about communication.

Nice to haves

• Experience generating synthetic data for a variety of use cases.

• Past experience in creating high-performance implementations of deep learning algorithms

• Familiarity with:

• Slurm, MPI, Ray, Distributed Systems, Model Parallelism, FSDP

• Experience working remotely in a distributed company

At Gretel, we believe that the best ideas come from the blending of diverse perspectives and experiences, which will lead to a stronger company and advancements in technologies. We hire individuals whose peers call them subject matter experts, whose curiosity draws them to new edges of their field and who like to laugh. We are deeply collaborative, apolitical and mission-oriented.

Gretel is an equal opportunity employer. Individuals seeking employment and employees at Gretel are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status, or any other characteristic protected by applicable law.

Compensation

Employee compensation will be determined based on interview performance, level of experience, specialization of skills, and market rate. During the offer discussion, your recruiter will review the finalized base salary, bonus (for applicable roles), benefits and perks (additional information available on our career site), and stock options as they’ll be reflected in the offer letter.