Solutions Engineer at DatologyAI

Compensation

$230,000 – $300,000/yr

Setup: Hybrid
Location: Redwood City, California
Type: Full-time
Posted: Jan 9, 2026

Description

DatologyAI is seeking a highly technical Solutions Engineer with deep ML and AI platform experience to support customers in a pre-sales role. The successful candidate will partner with strategic prospects to understand their data curation needs, technical constraints, and business goals, designing scalable solutions that demonstrate the impact of DatologyAI’s platform. This role requires hands-on understanding of modern LLM/VLM training and evaluation, working 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 training lifecycle.

Required Skills

ML Platform
AI Platform
Pre-sales
LLM Training
VLM Training
Model Evaluation
Python
Spark
Ray
PyTorch
Hugging Face
AWS
GCP
Azure
Docker
Kubernetes

Additional Information

Required Previous Experiences: 4+ years of experience in software, ML platform, solutions, or customer engineering roles, with significant experience driving technical pre-sales engagements and PoCs.
Preferred Qualifications: 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 metrics/benchmarks, building or using evaluation harnesses, analyzing results, and presenting findings clearly with customers.
Strong programming skills in Python (or equivalent); able to prototype quickly and iterate with customers.
Experience with data processing / distributed systems (e.g., Spark, Ray, data lakes/warehouses) and comfort working with large-scale datasets.
Familiarity with modern ML infrastructure: PyTorch/Hugging Face ecosystems, distributed training concepts, and deployment environments across cloud/on-prem/hybrid.
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.