Datology AI

Datology AI

Solutions Engineer (AI/ML, Pre-Sales)

Redwood City · Mid

Sponsorship not specified$230k-$300kDetected 193 days ago
PythonDistributed SystemsAWSGCPAzureCloud PlatformsDockerKubernetesMachine LearningDeep LearningPyTorchSparkData EngineeringLLMsSalesResearchCommunicationPublic Speaking

About the role

  • This role requires strong hands-on understanding of modern LLM/VLM training and evaluation.
  • 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.

Responsibilities

  • 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.
  • 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

Requirements

  • 4+ years of experience in software, ML platform, solutions, or customer engineering roles, with significant experience driving technical pre-sales engagements and PoCs.
  • Experience with data processing / distributed systems (e.g., Spark, Ray, data lakes/warehouses) and comfort working with large-scale datasets.
  • Familiarity with cloud platforms (AWS/GCP/Azure) and containerization (Docker/Kubernetes).
  • Strong communication skills, with the ability to translate complex ML and systems topics for diverse audiences.

Compensation

  • At DatologyAI, we are dedicated to rewarding talent with competitive salary and meaningful equity. The salary for this position ranges from $230,000 to $300,000 OTE.
  • Starting pay is based on job-related skills, experience, qualifications, and interview performance.
  • 401(k) plan with a generous 4% company match.
  • Annual $2,000 wellness stipend.
  • Annual $1,000 learning and development stipend.
  • Daily lunches and snacks are provided in our office!

Benefits

  • 100% covered health benefits (medical, vision, and dental).
  • Unlimited PTO policy
  • Paid Parental Leave of 12 weeks, plus 6 months of WFH flexibility.
  • Annual $2,000 wellness stipend.
  • Annual $1,000 learning and development stipend.
  • Our benefits are built to support your well-being and growth:

Company info

  • 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.
  • For more details, check out our recent research on synthetic data scaling (BeyondWeb https://www.datologyai.com/blog/beyondweb) and pretraining with domain-specific data (The Finetuner's Fallacy https://www.datologyai.com/blog/finetuners-fallacy).
  • We raised a total of $57.5M in two rounds, a Seed and Series A.
  • This role is based in Redwood City, CA.
  • We are in office 4 days a week.
  • 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.
  • 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.
  • Embed deeply with strategic customers to understand their data curation needs, business challenges, and technical requirements in detail.
  • Examples of the kinds of practical deep learning questions you might have to answer for customers:
  • We are looking for a highly technical Solutions Engineer with deep ML and AI platform experience to support customers in a pre-sales role.
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