Solutions Engineer

About the Role We are seeking a highly technical Solutions Engineer with deep ML and AI platform experience to support customers in pre-sales engagements. You will partner with strategic prospects to understand their data curation needs and design scalable solutions that demonstrate the impact of DatologyAI's platform. This role requires hands-on experience with modern LLM/VLM training and evaluation and the ability to design PoCs that link data decisions to measurable model outcomes across the full training lifecycle. What You'll Do

• Embed deeply with strategic customers to understand 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 (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. What We're Looking For
• 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 metrics/benchmarks, building or using evaluation harnesses, analyzing results, and presenting findings clearly with customers. Examples of practical deep learning questions you might need to answer include: What’s the difference between MMLU, MMMU, and MMMLU? Is SWE-Bench a useful eval for base models? What’s a standard context window for pretraining, and what are the costs and benefits of changing it? What’s the difference between CPT and midtraining? Can we just compare your data to Qwen3? Will your data work with our model architecture?
• 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.
• Required to travel to customer sites as needed to support pre-sales engagements. Compensation & Benefits
• Salary: $230,000 - $300,000 OTE per year (USD).
• 100% covered health benefits (medical, vision, and dental).
• 401(k) plan with a generous 4% company match.
• Unlimited PTO policy.
• Paid Parental Leave of 12 weeks, plus 6 months of WFH flexibility.