Gravity Lending

Machine Learning Engineer (Dark Horse AI Platform)

Location: Austin, TX (Hybrid – 3 days in office)
Team: Development & Engineering
Reports to: CTO

About the Role

Gravity Lending is building Dark Horse, our internal AI platform powering intelligent workflows across our lending ecosystem — including our Loan Origination System, internal tools, and customer-facing portals.

We’re looking for a Machine Learning Engineer to design, build, and operate production-grade ML, LLM capabilities, and AI Agent–driven systems in a regulated FinTech environment. This is not a research role or demo lab — this is real-world AI deployed at scale, with real data, real customers, and real compliance requirements.

You’ll partner closely with Product, Engineering, and Security to deliver AI systems that:

• Retrieve and reason over complex application and loan data (RAG)
• Maintain reliable, auditable chat and decision workflows
• Support risk assessment, decisioning, and automation
• Meet strict privacy, security, and compliance standards (PII, SOC-minded controls)

If you enjoy building secure, reliable, production ML systems — and want your work to directly impact revenue, efficiency, and customer outcomes — this role is for you.

What You’ll Do

Build & Operate AI Capabilities

• Design and productionize ML/LLM features for Dark Horse, including:
• Retrieval-Augmented Generation (RAG) pipelines
• Tool / function calling patterns
• Chat session memory and state strategies
• Safe retrieval and response patterns
• Develop ML services supporting decision support, automation, and intelligent workflows.

Data & Feature Engineering

• Design data and feature pipelines for credit and lending use cases, with:
• Data quality checks
• Lineage and reproducibility
• Clear separation of training vs inference data
• Partner with stakeholders to ensure models align with business and risk requirements.

MLOps & Reliability

• Build and deploy batch and real-time ML services with monitoring for:
• Model drift
• Latency and availability
• Accuracy and cost
• Establish and mature MLOps practices, including:
• Model versioning and registries
• CI/CD for ML pipelines
• Evaluation harnesses
• Rollback and canary release strategies

Security & Compliance

• Implement guardrails for regulated data, including:
• PII redaction and access controls
• Prompt safety patterns
• Audit logging and traceability
• Secure secrets and key management
• Collaborate with Security to ensure privacy-by-design across all AI systems.

Cross-Functional Collaboration

• Partner with backend engineers to integrate ML services into:
• PHP-based REST systems
• MySQL-driven workflows
• Define success metrics and evaluation strategies with Product and Engineering leaders.
• Contribute to architectural decisions and platform standards.

Required Qualifications

• 3–6+ years of experience in ML engineering or applied ML in production environments
• Strong Python skills and experience building ML services or APIs
• Hands-on experience deploying LLMs in production, including:
• RAG architectures
• Embeddings and reranking
• Prompt and tool orchestration
• Model evaluation
• Experience with modern MLOps, including:
• Docker
• CI/CD pipelines
• Model registries
• Monitoring and alerting
• Solid understanding of data security fundamentals:
• Least-privilege access
• Secrets management
• Encryption
• Audit trails
• Comfortable working cross-functionally in a product-driven engineering organization

Nice to Have

• FinTech, credit, or lending experience (risk scoring, underwriting, fraud, decisioning)
• Experience with vector databases and search:
• Hybrid retrieval
• Query rewriting
• Relevance tuning
• Familiarity with SOC 2, privacy-by-design, and handling PII at scale
• Experience integrating ML services with PHP backends and MySQL-heavy systems
• Experience optimizing inference cost and latency:
• Caching
• Batching
• Quantization
• Intelligent routing

Tech You’ll Likely Use

Python, FastAPI, Docker, Kubernetes (or ECS), MLflow (or similar), vector databases/search, SQL/MySQL, message queues, GitHub Actions, observability tools (logs, metrics, traces), and AWS cloud services.

Why Gravity Lending

• High-impact AI work directly tied to revenue, efficiency, and customer outcomes
• Real ownership over architecture, evaluation standards, and production reliability
• A security-first engineering culture — solving real problems, not building demo-ware
• A growing FinTech platform where AI is a core differentiator, not a side project

Compensation (Austin Base Salary)

• Minimum: $125,000
• Target: $1,000
• Maximum: $165,000
• (Final compensation based on experience and alignment with role scope.)

Pay: $125,000.00 - $165,000.00 per year

Benefits:

• 401(k)
• Dental insurance
• Employee assistance program
• Employee discount
• Flexible spending account
• Health insurance
• Health savings account
• Life insurance
• Paid time off
• Vision insurance

Work Location: Hybrid remote in Austin, TX 78728