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Salary: USD 187,000 - 229,000 / annual
EarnIn is seeking a Machine Learning Engineer to join its AI/ML platform team. You will develop, train, deploy, and evaluate machine learning models that power user-facing financial products—from predictive models on transaction and behavioral data to agentic applications built on large language models. Your work directly supports EarnIn's mission to provide fair and intelligent financial tools to millions of paycheck-to-paycheck users.
Key responsibilities include:
- Developing and training ML models (sequence, embedding, classification) on large-scale financial and behavioral datasets
- Building feature and data pipelines that transform raw event data into production-ready training datasets
- Designing offline and online evaluation frameworks: success metrics, backtests, A/B tests, error analysis, and regression suites
- Owning models in production: serving infrastructure, latency and cost optimization, retraining loops, and monitoring for drift
- Fine-tuning and adapting LLMs for internal use cases, including prompt engineering, memory/context pipelines, retrieval, and tool integrations
- Building backend services and RESTful APIs in Python to expose models and agentic applications to internal and product surfaces
- Instrumenting pipelines for observability: logging, tracing, and distributed monitoring across model and agent workflows
- Collaborating cross-functionally with ML engineers, data scientists, and product teams to shape intelligent and safe AI features
Required qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or related field (or equivalent experience)
- 2+ years of industry experience building and shipping ML systems
- Strong Python proficiency and hands-on experience with PyTorch and the standard ML stack (NumPy, pandas, scikit-learn)
- Experience with large-scale data processing (Spark, Databricks) and production feature engineering
- Solid ML fundamentals: model architecture, training dynamics, regularization, and debugging
- Experience designing evaluation frameworks for ML systems and LLM behavior
- Working knowledge of LLM APIs (OpenAI, Claude), prompt engineering, and agentic frameworks
- Experience with API design, async workflows, and production databases (SQL/NoSQL)
- Familiarity with AI-assisted development tools (GitHub Copilot, Cursor, ChatGPT)
Preferred: LLM fine-tuning experience (Unsloth, Axolotl, HuggingFace PEFT), distributed training, MLOps tooling (MLflow, Weights & Biases, Feast), vector stores (Weaviate, Pinecone), and observability frameworks (OpenTelemetry).
