Machine Learning Engineer

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 work 2 days a week. WHAT YOU'LL DO

• Develop and train ML models — including sequence, embedding, and classification models — on large-scale financial and behavioral data.
• Build feature and data pipelines that turn raw event data into training-ready datasets, and keep training and serving features 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, tracing, and distributed monitoring across model and agent workflows.
• Collaborate cross-functionally with ML engineers, data scientists, and product to shape intelligent and safe AI features. WHAT WE'RE LOOKING FOR
• Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related field, or equivalent experience
• 2+ years of industry experience 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 tools) as part of your software development workflow
• Solid grounding in ML fundamentals: model architecture choices, training 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, Axolotl, LLaMA-Factory, or HuggingFace PEFT/TRL, including parameter-efficient methods (LoRA/QLoRA) is a plus
• Experience with distributed training or representation learning is a plus.
• Familiarity with MLOps tooling for experiment tracking, feature stores, or model registries (MLflow, Weights & Biases, Feast) is a plus.
• Familiarity with vector stores (e.g., Weaviate, Pinecone, Qdrant) is a plus
• Knowledge of OpenTelemetry or similar observability frameworks is a plus
• Exposure to container-based deployment or serverless environments (Docker, AWS Lambda, etc.).
• Background in fintech, fraud, risk, or credit modeling is a plus #LI-Hybrid