Paralegal (Atlanta)
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Salary: $140,000 - $180,000 per year A bit about us: We’re transforming how government agencies digitize forms and automate workflows. Our new initiative brings AI directly into this process - using LLMs, vector search, and structured PDF parsing to accelerate public service delivery. We’re not just bolting AI onto the side. It’s becoming core to how our platform works. We’re looking for a senior machine learning engineer to take the lead on this effort. You’ll be the architect of our AI capability - not just a contributor. Your work will touch thousands of public-facing government forms, helping real people get things done faster and more accurately. This isn’t an R&D team running experiments - it’s about delivering intelligent automation, right now. Why join us?
- 100% remote based in the US
- Help shape the AI transformation of public sector services
- Lead initiatives that ship real impact, not just prototypes
- Greenfield development on a proven, profitable platform
- Comprehensive Health, Vision, Dental coverage for individuals and families
- Build and tune vector-based retrieval pipelines using OpenAI embeddings and Azure AI Search
- Design prompt strategies and agents to translate parsed PDF data into form component schemas
- Fine-tune LLMs for structured output generation with low-latency performance in mind
- Lead the development of an RLHF loop that incorporates builder UI feedback and audit data
- Help architect systems that blend traditional APIs and probabilistic inference reliably
- Work alongside full-stack and platform engineers to get it all running in production
- Stay plugged into the latest model capabilities, and make smart calls on what to adopt
- Azure AI Studio, Azure OpenAI, GPT-4o
- Python (for agents, functions, orchestration), .NET 8 (for integration layers)
- Azure AI Search, CosmosDB, MSSQL
- Kubernetes (AKS), Azure Blob, Octopus for CI/CD
- Extend.ai for structured PDF parsing
- 5+ years in applied ML, including experience with retrieval, embeddings, and prompt engineering
- Strong Python skills and familiarity with production-grade ML pipelines
- Experience designing and tuning RAG workflows with hybrid search
- Familiarity with RLHF and fine-tuning on structured JSON output
- Solid grasp of system-level thinking—how to bring ML into product environments cleanly
- You’ve shipped a working vector search + RAG pipeline integrated into our form builder
- You’ve scoped and kicked off our first LLM fine-tuning cycle
- We’re collecting human feedback to improve model accuracy
- You’ve helped define the roadmap for AI integrations across the platform
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