Machine Learning Engineer

EarnIn•Mountain View, CA

2d•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

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

Responsibilities

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.