About THIA
THIA is transforming how small and medium enterprises build internal applications and automate business processes. Our AI-powered platform enables business experts to create custom applications using natural language, eliminating the need for expensive development teams. We're well-funded, generating revenue, and solving real problems for companies that need more than off-the-shelf software.
The Role
This is a generalist ML role for someone who wants depth in modeling and pragmatism about everything else it takes to ship. You'll pick up ML features end to end - framing the problem, picking the right approach (fine-tune, prompt, retrieve, or something cheaper), building the eval that tells us it works, and getting it into production. You'll work closely with a small, senior team and have real influence on how we build.
We move fast, keep our codebase clean, and take tech debt seriously.
What You'll Do
ML Engineering
- Ship ML features end to end - problem framing, modeling, evaluation, deployment, iteration
- Make pragmatic tradeoffs across fine-tuning, prompting, and retrieval based on what the problem actually needs
- Build and extend evaluation pipelines: offline metrics, regression detection, eval datasets
- Work with cloud ML infrastructure (training, serving, monitoring)
- Help drive concrete quality gains through eval results, customer feedback, and prompt iteration
Collaboration
- Work autonomously while staying tightly coordinated with a small, async-first team
- Partner with the platform team on serving and observability, and with product on what to ship next
- Contribute to architectural decisions and internal documentation
What We're Looking For
Must-Haves
- Strong Python; comfortable with at least one deep-learning framework (PyTorch preferred)
- Has trained or fine-tuned transformer-based models and seen them deployed
- Has built or substantially contributed to a model-evaluation pipeline
- Comfortable with cloud ML infrastructure (training, serving, monitoring)
- Experience collaborating with product or domain experts to ship real features
Strongly Preferred
- LLM eval / observability work (LLM-as-judge, trace enrichment)
- RAG / retrieval system experience
- MLOps tooling (model registries, feature stores, experiment tracking)
You Don't Need
- Mastery of every part of the ML stack - depth in one area + working knowledge across the rest is the target
How We Evaluate
We hire for skill and potential, however acquired. If you can do the work, we want to hear from you.
A Note on AI
We actively encourage using AI tools to move faster. Real-world experience is still required — to direct AI effectively, catch what it misses, and spot security issues before they reach production.
Our Stack
Python · PyTorch · HuggingFace · Modal · GCP · PostgreSQL / SQLite · Qdrant · Redis · Docker · GitLab CI/CD · Datadog
What You Gain
- Ownership - end-to-end accountability for ML features at a growing AI company
- Impact - direct collaboration with leadership and real influence on technical direction
- Growth - clear path to a lead role as the team expands
- Equity - early-stage equity at an AI startup
- Flexibility - fully remote with flexible hours
- Quality - a clean codebase and a team that takes tech debt seriously
