Machine Learning Engineer
Earnin · Mountain View, US
About this role
**Machine Learning Engineer — EarnIn (AI/ML Platform Team)** **About EarnIn** EarnIn is a pioneer of earned wage access, building products that deliver real-time financial flexibility for people living paycheck to paycheck. Our community can access earnings as they earn them—spending, saving, and growing money without mandatory fees, interest rates, or credit checks. We’re backed by world-class funding partners (A16Z, Matrix Partners, DST, Ribbit Capital) and have a strong core business with tremendous runway. We’re growing fast and looking for world-class talent to help shape our next chapter. **Position Summary** EarnIn is 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 using transaction and behavioral data to **agentic applications built on large language models**. **Location / Work Style** - **Hybrid**: Mountain View (Headquarters) - **In-office**: 2 days/week **Compensation** - Base salary range: **$187,000–$229,000** (plus equity and benefits) --- ## What You’ll Do - Develop and train **ML models** (sequence, embedding, classification) on large-scale financial and behavioral data. - Build **feature and data pipelines** to create training-ready datasets and keep training/serving features consistent. - Design **offline and online evaluation** for models and agentic workflows (success metrics, backtests, A/B tests, error tracing, regression suites). - Take models to production: serving infrastructure, **latency/cost tuning**, retraining loops, and monitoring for drift/performance degradation. - Fine-tune and adapt **LLMs** for internal use cases; build orchestration (prompting, memory/context pipelines, retrieval, tool integrations). - Build **backend services and RESTful APIs** in Python to expose models and agentic applications to internal tools and product surfaces. - Instrument pipelines for **observability** (logging, tracing, distributed monitoring across model/agent workflows). - Collaborate cross-functionally with ML engineers, data scientists, and product to build intelligent and safe AI features. --- ## What We’re Looking For - Bachelor’s or Master’s in CS/Engineering/Statistics (or equivalent experience). - **2+ years** building and shipping ML systems. - Strong **Python** and hands-on experience with **PyTorch** and the standard ML stack (NumPy, pandas, scikit-learn). - Experience using AI-assisted development tools (e.g., GitHub Copilot, Cursor, ChatGPT, or similar). - Solid ML fundamentals (architecture choices, training dynamics, regularization, diagnosing learning issues). - 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 equivalent). - Experience with API design, async workflows, and production database usage (**SQL or NoSQL**). - Clear communication and a collaborative mindset. **Nice to have** - LLM fine-tuning frameworks (Unsloth, Axolotl, LLaMA-Factory, HuggingFace PEFT/TRL) and parameter-efficient methods (LoRA/QLoRA). - Distributed training or representation learning. - MLOps tooling (MLflow, Weights & Biases, Feast). - Vector d
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