Partner with frontier AI research labs to design datasets and environments that improve model performance
Lead technical conversations with customer researchers to understand model capabilities, failure modes, data requirements, and success criteria
Probe model behavior through systematic evaluation to uncover weaknesses and identify high-impact data interventions
Design evaluation frameworks, calibration processes, and quality rubrics that establish measurable project success metrics
Develop technical specifications for data projects that balance research rigor with operational feasibility
Serve as thought partner to customer research teams throughout the sales cycle, building trust and credibility
Stay current on frontier AI research, RL environment design, post-training techniques, and evaluation methodologies

Strong expertise in frontier AI concepts including LLMs, training data pipelines, evaluation methodologies, post-training techniques (RLHF, DPO, RLAIF), and domain areas such as coding agents, reasoning, multimodal models, or RL environments
Experience in applied ML research, data science, or research-intensive technical roles with customer-facing or collaborative research experience
Proficiency in Python and familiarity with ML frameworks and LLM APIs
Excellent communication skills — ability to deliver technical presentations and explain complex concepts to diverse audiences
Familiarity with data curation workflows, synthetic data generation, LLM-as-a-Judge, or evaluation framework design
Ability to work in a fast-moving environment, comfortable with ambiguity and rapid iteration
B.S. in Computer Science, Machine Learning, or related field with 4+ years of experience in AI/ML solutions engineering or technical customer-facing roles

AI Solutions Engineer – Data as a Service

Key skills