What are the responsibilities and job description for the Senior Solutions Engineer position at Harell Data?
About Harell Data
AI has transformed digital industries, but progress in the physical sciences — drug discovery, materials science, climate modeling — has stalled. The bottleneck isn't compute or algorithms. It's data. The most valuable scientific datasets are locked in silos, unstructured, and inaccessible.
We're fixing that. Harell Data is a managed platform where organizations can securely share proprietary datasets, train models on high-performance GPUs, and deploy them for inference and application development. Dataset owners share their data for model training without giving up control of it. Researchers and engineers get access to compute to train, deploy, and use their models. It's the infrastructure layer that turns scattered scientific data into domain-specific foundation models.
About the Role
You'll be the founding Solutions Engineer at Harell Data and your job is to make our customers wildly successful on the platform — from first login to production workloads. Our customers are a mix of computational scientists, bioinformaticians, and ML engineers working on hard problems in drug discovery, protein modeling, and materials science. You'll be the connective tissue between what they're trying to accomplish and what our platform can do. This is a founding role. You'll shape how we work with customers, define the solutions engineering function from scratch, and have a direct line into product and infrastructure decisions.
What You Will Do
- Own customer onboarding and activation. Build the repeatable system that takes customers from signed contract to running workloads — onboarding flows, getting-started guides, and self-serve resources that shrink time-to-first-training-job without requiring your involvement every time.
- Be the technical partner customers rely on. Work directly with researchers and ML engineers to design training workflows, configure fine-tuning pipelines, set up inference endpoints, and troubleshoot issues across the stack — from data ingestion to GPU utilization.
- Build reference architectures and technical content. Create sample notebooks, workflow templates, integration guides, and best-practice documentation that help customers self-serve and reduce repeated support burden.
- Drive the feedback loop into product. Synthesize patterns from customer interactions into clear, prioritized input for engineering. You'll be in the room when we make build-vs-buy decisions and set the product roadmap.
- Develop scalable engagement models. As the customer base grows, define when to go high-touch vs. self-serve. Build the internal systems, runbooks, and escalation paths that let solutions engineering scale beyond you.
Qualifications
- 5 years in a customer-facing technical role — solutions engineering, technical account management, developer relations, or applied engineering where you worked directly with external users.
- Comfortable with ML training and inference workflows (PyTorch, Hugging Face, or similar). You don't need to be an ML researcher, but you should be able to help someone debug why their job isn't working.
- Working knowledge of cloud infrastructure (AWS, GCP, or similar)
- Strong written and verbal communication — you'll be the bridge between customers and our engineers.
- Comfortable with ambiguity. We're early. If the doc doesn't exist, you write it. If the demo environment isn't set up, you build it. You've done this before and you like it.