Software Engineer, Training & Inference Infrastructure

About the Company

Models are what they eat. But a large portion of training compute is wasted training on data that are already learned, irrelevant, or even harmful, leading to worse models that cost more to train and deploy.

At DatologyAI, we’ve built a state of the art data curation suite to automatically curate and optimize petabytes of data to create the best possible training data for your models. Training on curated data can dramatically reduce training time and cost (7-40x faster training depending on the use case), dramatically increase model performance as if you had trained on >10x more raw data without increasing the cost of training, and allow smaller models with fewer than half the parameters to outperform larger models despite using far less compute at inference time, substantially reducing the cost of deployment. For more details, check out our recent blog posts sharing our high-level results for text models and image-text models.

We raised a total of $57.5M in two rounds, a Seed and Series A. Our investors include Felicis Ventures, Radical Ventures, Amplify Partners, Microsoft, Amazon, and AI visionaries like Geoff Hinton, Yann LeCun, Jeff Dean, and many others who deeply understand the importance and difficulty of identifying and optimizing the best possible training data for models. Our team has pioneered this frontier research area and has the deep expertise on both data research and data engineering necessary to solve this incredibly challenging problem and make data curation easy for anyone who wants to train their own model on their own data.

This role is based in Redwood City, CA. We are in office 4 days a week.

About the Role

We’re looking for an engineer with deep experience building and operating large-scale training and inference systems. You will design, implement, and maintain the infrastructure that powers both our internal ML research workflows and the high-performance inference pipelines that deliver curated data to our customers.

As one of our early hires, you will influence technical direction, partner directly with researchers and product engineers, and take ownership of systems that are central to our company’s success.

What You'll Work On

• Architect and maintain training infrastructure that are reliable, scalable, and cost-efficient.

• Build robust model serving infrastructure for low-latency, high-throughput inference across heterogeneous hardware.

• Automate resource orchestration and fault recovery across GPUs, networking, OS, drivers, and cloud environments.

• Partner with researchers to productionize new models and features quickly and safely.

• Optimize training and inference pipelines for performance, reliability, and cost.

• Ensure all infrastructure meets the highest bar for reliability, security, and observability.

About You

• Have at least 5 years of professional software engineering experience.

• Expertise in Python and experience with deep learning frameworks (PyTorch preferred)

• Have an understanding of modern ML architectures and an intuition for how to optimize their performance, particularly for training and/or inference

• Have familiarity with inference tooling like vLLM, SGLang, or custom model parallel systems.

• Proven experience designing and running large-scale training or inference systems in production.

• Have or can quickly gain familiarity with PyTorch, NVidia GPUs and the software stacks that optimize them (e.g. NCCL, CUDA), as well as HPC technologies such as InfiniBand, NVLink, AWS EFA etc.

• Commitment to engineering excellence: strong design, testing, and operational discipline.

• Collaborative, humble, and motivated to help the team succeed.

• Ownership mindset: you’re comfortable learning fast and tackling problems end-to-end.

Don’t meet every single requirement? We still encourage you to apply. If you’re excited about our mission and eager to learn, we want to hear from you!

Compensation

At DatologyAI, we are dedicated to rewarding talent with highly competitive salary and significant equity. The base salary for this position ranges from $180,000 to $300,000.

• The candidate's starting pay will be determined based on job-related skills, experience, qualifications, and interview perfo