Machine Learning Engineer

About EarnIn As one of the first pioneers of earned wage access, our passion at EarnIn is building products that deliver real-time financial flexibility for those with the unique needs of living paycheck to paycheck. Our community members access their earnings as they earn them, with options to spend, save, and grow their money without mandatory fees, interest rates, or credit checks. We’re fortunate to have an incredibly experienced leadership team, combined with world-class funding partners like A16Z, Matrix Partners, DST, Ribbit Capital, and a very healthy core business with a tremendous runway. We’re growing fast and are excited to continue bringing world-class talent onboard to help shape the next chapter of our growth journey. POSITION SUMMARY We're seeking a Machine Learning Engineer to join our AI/ML platform team. You'll train, deploy, and evaluate models that power user-facing financial products — from predictive models over transaction and behavioral data to agentic applications built on large language models. Your work will support EarnIn's mission to provide fair and intelligent financial tools to millions of users. The base salary range for this full-time position is $187,000–$229,000, plus equity and benefits. Our salary ranges are determined by role, level, and location. This is a hybrid position in Mountain View (Headquarters) and will require in-office wo [... source excerpt omitted ...] es consistent. Design offline and online evaluation for models and agentic workflows: success metrics, backtests, A/B tests, error tracing, and regression suites. Take models to production and own them there — serving infrastructure, latency and cost tuning, retraining loops, and monitoring for drift and performance degradation. Fine-tune and adapt LLMs for internal use cases, and build the orchestration around them: prompting, memory and context pipelines, retrieval, and tool integrations. Build backend services and RESTful APIs in Python that expose models and agentic applications to internal tools and product surfaces. Instrument pipelines for observability — logging, traci [... source excerpt omitted ...] g dynamics, regularization, and how to diagnose a model that isn't learning Experience with large-scale data processing (Spark, Databricks, or similar) and feature engineering on production data. Experience designing evaluation for ML systems and LLM behavior — metrics, automated checks, offline test harnesses, and behavioral regression suites Working knowledge of LLM APIs (e.g., OpenAI, Claude), prompt engineering, and at least one agentic framework or custom equivalent. Experience with API design, async workflows, and production database usage (SQL or NoSQL). Clear communication and a collaborative mindset. Experience with LLM fine-tuning using frameworks such as Unsloth, A