Senior Solutions EngineerAbout Harell Data

About Harell Data

AI transformed digital industries. Progress in physical sciences stalled: drug discovery, materials

science, climate modeling. The bottleneck isn't compute or algorithms. It's data. The best

scientific datasets sit locked in silos, unstructured and unusable.

Harell Data fixes this. Organizations share proprietary datasets securely, train models on high-

performance GPUs, and deploy them for inference. Data owners keep control of their data and

get paid when it drives a breakthrough.

About the Role

You'll be one of our first Solutions Engineers. Your job: make customers succeed on the

platform, from first login to production workloads.

Customers are computational scientists, bioinformaticians, and ML engineers solving hard

problems in drug discovery and protein modeling. You connect what they need to what our

platform does.

What You Will Do

Your Goal - Build a scalable, repeatable technical onboarding machine with your time split across three pillars:

• Customer Enablement & Onboarding - Help customers move their datasets onto the platform. Shrink their time-to-first-training-job with getting-started guides, sample notebooks, and self-serve resources.

• Technical Troubleshooting - Act as the trusted technical partner for external researchers. Debug PyTorch and Hugging Face job configuration errors, resolve pipeline blockages, and optimize GPU workloads.

• Product Feedback Loop - Synthesize common technical hurdles and feature requests from customer interactions. Work with core engineering to shape build-vs-buy decisions and the product roadmap.

Qualifications

We're looking for an infrastructure-adjacent builder who loves helping customers solve technical

problems.

• 5+ years in a customer-facing technical role (Solutions Engineering, Technical Account Management, Support Engineering, or Developer Relations)

• Hands-on comfort with ML frameworks. Ability to read and debug Python code using PyTorch, Hugging Face, or similar standard ML training workflows.

• Cloud fundamentals. Solid understanding of cloud infrastructure basics (AWS, GCP, Linux, or basic containerization/Docker).

• Strong communication. Clear written skills for technical documentation, runbooks, and integration guides.

• Startup mindset. Comfortable with ambiguity, writing the first draft of missing documentation, and building initial demo environments.

Bonus Points (Not Required)

Scientific Domain Exposure. Experience or comfort working alongside scientific domains (bioinformatics, biotech, computational chemistry, or heavy data-science fields).

No science degree needed, just the curiosity to learn their workflows.

Location note: this role is based in Bellevue, WA or Palo Alto, CA. No relocation assistance available for this role.