Datology AI
Solutions Engineer (AI/ML, Pre-Sales)
About this role
DatologyAI seeks a technically expert Solutions Engineer to guide strategic customers through pre-sales engagements, designing and executing proof-of-concept projects that demonstrate how the company's data curation platform reduces training costs and improves model performance. You'll work hands-on with customer ML teams to connect data curation decisions to measurable improvements in model quality and efficiency across the full training lifecycle.
What you'll do
- Conduct deep technical discovery with strategic customers to understand their data curation needs, constraints, and business goals
- Design and lead end-to-end PoCs that test data curation impact on model quality, training speed, and deployment efficiency
- Partner with customer ML teams to plan datasets and curation strategies aligned with their training objectives
- Design evaluation plans, select appropriate benchmarks and metrics, and execute model evaluations for base and post-trained models
- Produce comprehensive evaluation reports documenting methodology, results, ablations, and productionization recommendations
- Communicate technical findings and tradeoffs to both ML engineers and executive stakeholders across the customer organization
What they're looking for
- ML model training expertise (pre-training, fine-tuning, reinforcement learning)
- Model evaluation and benchmarking design
- LLM/VLM architecture and training knowledge
- Technical pre-sales and proof-of-concept execution
- Data analysis and interpretation
- Customer communication and technical presentation
- Software engineering fundamentals
- Python or similar ML engineering languages
Benefits
- Work with cutting-edge AI/ML research and data curation technology
- 4 days per week in-office at Redwood City headquarters
- Collaborate with world-class researchers and investors (Geoff Hinton, Yann LeCun, Jeff Dean)
- Direct impact on strategic customer outcomes and product direction
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Datology AI
DatologyAI builds a data curation platform that optimizes AI training datasets to reduce costs and improve model performance. The company is hiring cloud infrastructure engineers, data platform engineers, full-stack product engineers, and solutions engineers to scale its multi-cloud infrastructure and customer-facing tools.
View all jobs at Datology AILikely interview questions
- Walk us through a PoC you've led with a customer where you had to design an evaluation plan to measure model improvements. How did you choose your metrics and benchmarks, and how did you communicate the results to both technical and executive stakeholders?
- Describe your hands-on experience with LLM/VLM training across pre-training, mid-training (domain-specific), and post-training phases. What are the key differences in how you'd approach data curation decisions at each stage?