Solutions Engineer; AI​/ML, Pre-Sales

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 Datology

AI, 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.

We are looking for a highly technical Solutions Engineer with deep ML and AI platform experience to support customers in a pre-sales role. In this role, you will partner closely with our most strategic prospects to deeply understand their data curation needs, technical constraints, and business goals, and to design scalable solutions that demonstrate the impact of Datology

AI’s platform.

This role requires strong hands-on understanding of modern LLM/VLM training and evaluation. You will work directly with customer ML teams to design PoCs that connect data curation decisions to measurable outcomes in model quality, training efficiency, and downstream performance—across the full lifecycle of training (pre-training, mid-training, and post-training), and with rigorous evaluation plans and reporting.

Embed deeply with strategic customers to understand their data curation needs, business challenges, and technical requirements in detail.

Lead end-to-end customer PoCs that connect data curation, training behavior, evaluation outcomes, including dataset analysis, training plan design, and results interpretation.

Partner with customer ML teams to map data & curation strategy

Design and execute evaluation plans for base and post-trained models, selecting appropriate benchmarks/metrics, and running model evaluations

Produce customer-ready evaluation reports: methodology, metrics, baselines, ablations (e.g., curated vs raw), conclusions, and recommended next steps for productionization.

Communicate technical results to both ML experts and exec stakeholders, including tradeoffs in compute, latency, and deployment cost.

Collaborate closely with GTM, Engineering, and Research teams to ensure seamless customer experiences, deliver compelling demos, align on requirements, and bring customer insights into actionable model training and product strategies.

Provide technical guidance, training, and clear documentation to ensure prospects can confidently assess the solution.

4+ years of experience in software, ML platform, solutions, or customer engineering roles, with significant experience driving technical pre-sales engagements and PoCs.

Strong practical expertise in ML model training, including how models are trained and improved across pre-training, domain-specific mid-training, and post-training, such as supervised fine-tuning and reinforcement learning.

Demonstrated ability to design, run, and interpret model evaluations for base and post-trained models: choosing…