Description
Software Engineer, Applied AI
Location: Seattle Hybrid, San Francisco Hybrid
Department: Software Engineering
Location Type: HYBRID
Employment Type: FULL_TIME
About the Role
We’re hiring a Senior Software Engineer - ML to join our Applied AI team at Supio. You’ll work at the intersection of software engineering and AI—building, fine-tuning, and scaling ML systems that transform thousands of complex documents into structured, actionable data. This role is deeply hands-on: writing production-quality code, designing scalable pipelines, and shipping features that reach 100K+ users.
If you’re a builder who thrives on solving ambiguous, data-heavy problems using ML and large language models—and you love seeing your work operating in production at scale—this is for you.
Note: this role is an Applied AI engineer who can actually code at a senior software level, with strong applied ML/LLM experience but not research‑heavy or academic.
What You’ll Do
- Design and implement ML-powered systems that process large-scale document data (legal, medical, billing, etc.) from raw PDFs to structured insights.
- Fine-tune LLMs and build retrieval-augmented generation (RAG) and agentic systems for real-world use cases.
- Own code from prototype to production—writing performant, maintainable Python code and integrating ML services into Supio’s core platform.
- Develop and optimize end-to-end evaluation frameworks to measure accuracy, latency, and reliability.
- Collaborate with product, design, and backend teams to bring AI features to life for real user problems.
- Work across the stack when needed: backend APIs, model serving, cloud infrastructure, and system monitoring.
- Partner directly with David Brinda (Hiring Manager) and technical leadership to shape Supio’s ML roadmap.
What We’re Looking For
- Strong coding skills: You build production systems, not just research prototypes. Python expertise is required; experience with TypeScript or Go is a plus.
- Hands-on ML experience: You’ve fine-tuned models, shipped them into production, and maintained them at scale (100K+ MAU).
- Applied AI mindset: Comfortable with RAG systems, prompt engineering, agents, and data preprocessing/postprocessing for NLP tasks.
- Probabilistic thinking: You reason in uncertainty, understand performance tradeoffs, and are comfortable balancing precision and recall rather than fixed rules.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerized environments for deployment.
- Clear communicator who can explain design decisions and model behavior to teammates and leaders.
Nice to Have
- Experience in full-stack or backend engineering roles before moving into ML.
- Background in fintech, health tech, or document-heavy domains.
- Familiarity with scaling systems that process large, unstructured data (thousands of-page PDFs, mixed media, etc.).
- Grit, adaptability, and curiosity—competitive gamers or analytical thinkers who enjoy complex strategy problems tend to thrive here.
What You’ll Work On in the First 6 Months
- Building ML pipelines for classification, clustering, and semantic matching.
- Deploying and monitoring fine-tuned LLMs for document summarization and case analysis.
- Implementing Supio’s homegrown RAG framework across major client streams.
- Delivering measurable impact by increasing model reliability and reducing processing time.
How Leveling Works
We intentionally hire this role across multiple levels. During the interview process, we assess technical depth, ownership, system thinking, and impact. Final leveling and compensation are determined based on demonstrated ability and experience.
Compensation
As an early-stage startup, we offer a competitive compensation package that includes base salary, meaningful equity, and benefits. Equity grants are designed to ensure employees share in the long-term success and upside of the company.
Base Salary: $190,000 - $255,000 annually
Actual compensation may vary outside of these ranges based on a number of factors, including a candidate’s qualifications, skills, competencies, experience, and geographic location.
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