# Machine Learning Engineer

**Company:** [Earnin](https://hotfix.jobs/companies/earnin)
**Location:** Mountain View, CA
**Role:** ML Engineering
**Salary:** $187k – $229k/yr
**Experience:** 2+ years
**Skills:** Python, PyTorch, NumPy, pandas, scikit-learn, Spark, Databricks, LLM APIs, Prompt Engineering, REST APIs, SQL, NoSQL, Docker, MLflow, OpenTelemetry
**Posted:** 2026-09-08

> Machine learning engineer who trains, evaluates, and productionizes models and LLM-powered applications for financial products. Requires 2+ years of ML systems experience, strong Python and PyTorch skills, production data pipelines, model evaluation, and API development.

## Job Description

## Responsibilities
- Develop and train sequence, embedding, and classification machine-learning models on large-scale financial and behavioral data.
- Build feature and data pipelines that transform raw event data into training-ready datasets and maintain consistency between training and serving features.
- Design offline and online evaluation for models and agentic workflows, including success metrics, backtests, A/B tests, error tracing, and regression suites.
- Deploy models to production and own serving infrastructure, latency and cost optimization, retraining loops, and monitoring for drift and performance degradation.
- Fine-tune and adapt large language models and build orchestration involving prompting, memory and context pipelines, retrieval, and tool integrations.
- Build Python backend services and RESTful APIs for models and agentic applications.
- Instrument pipelines with logging, tracing, and distributed monitoring.
- Collaborate with ML engineers, data scientists, and product teams to develop intelligent and safe AI features.

## 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 machine-learning systems.
- Strong Python and hands-on experience with PyTorch, NumPy, pandas, and scikit-learn.
- Experience using AI-assisted development tools such as GitHub Copilot, Cursor, or ChatGPT.
- Strong understanding of model architecture, training dynamics, regularization, and model diagnostics.
- Experience with large-scale data processing and production feature engineering.
- Experience designing ML and LLM evaluation metrics, automated checks, offline test harnesses, and behavioral regression suites.
- Working knowledge of LLM APIs, prompt engineering, and an agentic framework or equivalent.
- Experience with API design, asynchronous workflows, and SQL or NoSQL databases.
- Clear communication and collaborative working style.

## Nice-to-haves
- LLM fine-tuning with Unsloth, Axolotl, LLaMA-Factory, or HuggingFace PEFT/TRL, including LoRA or QLoRA.
- Distributed training or representation learning.
- MLOps tools for experiment tracking, feature stores, or model registries, such as MLflow, Weights & Biases, or Feast.
- Vector stores such as Weaviate, Pinecone, or Qdrant.
- OpenTelemetry or similar observability frameworks.
- Container-based deployment or serverless environments, including Docker or AWS Lambda.
- Experience in fintech, fraud, risk, or credit modeling.

## Compensation
- Base salary: $187,000–$229,000, plus equity and benefits.

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