17 hours ago
Mountain View, CA, USAMid Level
H1B sponsor
Base Salary
$187k - $229k/yr
Responsibilities
- Develop and train sequence, embedding, and classification ML models on large-scale financial and behavioral data.
- Build feature and data pipelines that create training-ready datasets and maintain consistency between training and serving features.
- Design offline and online evaluation systems, including success metrics, backtests, A/B tests, error tracing, and regression suites.
- Deploy and operate models in production, including serving infrastructure, latency and cost tuning, retraining loops, and drift and performance monitoring.
- Fine-tune and adapt LLMs and build agentic orchestration using prompting, memory, context pipelines, retrieval, and tool integrations.
- Build Python backend services and RESTful APIs for models and agentic applications.
- Instrument model and agent workflows with logging, tracing, and distributed monitoring.
- Collaborate with ML engineers, data scientists, and product teams on intelligent and safe AI features.
Requirements
- Bachelor's or master's degree in Computer Science, Engineering, Statistics, or a related field, or equivalent experience.
- At least 2 years of industry experience building and shipping ML systems.
- Strong Python skills and hands-on experience with PyTorch, NumPy, pandas, and scikit-learn.
- Experience with AI-assisted development tools such as GitHub Copilot, Cursor, or ChatGPT.
- Knowledge of ML fundamentals including model architecture, training dynamics, regularization, and model diagnosis.
- Experience with large-scale data processing and feature engineering on production data, using Spark, Databricks, or similar tools.
- Experience designing evaluation for ML systems and LLM behavior, including metrics, automated checks, offline test harnesses, and behavioral regression suites.
- Working knowledge of LLM APIs such as OpenAI and Claude, prompt engineering, and an agentic framework or custom equivalent.
- Experience with API design, asynchronous workflows, and production SQL or NoSQL databases.
- Experience with LLM fine-tuning frameworks such as Unsloth, Axolotl, LLaMA-Factory, or HuggingFace PEFT/TRL, including LoRA or QLoRA, is a plus.
- Experience with distributed training or representation learning is a plus.
- Familiarity with experiment tracking, feature stores, or model registries such as MLflow, Weights & Biases, or Feast is a plus.
- Familiarity with vector stores such as Weaviate, Pinecone, or Qdrant is a plus.
- Knowledge of OpenTelemetry or similar observability frameworks is a plus.
- Exposure to container-based deployment or serverless environments such as Docker or AWS Lambda.
- Fintech, fraud, risk, or credit-modeling experience is a plus.
- Clear communication and a collaborative mindset.
Benefits
- Base salary is supplemented by equity and benefits.
- Hybrid position in Mountain View requiring in-office work 2 days per week.
Tech Stack
Categories
About EarnIn
EarnIn builds a mobile app for earned wage access and related financial tools for U.S. workers living paycheck to paycheck. It lets users access a portion of accrued wages before payday, plus features like credit monitoring, automated savings, low-balance alerts, and a debit card, with no mandatory fees or interest. Founded in 2012 and headquartered in Mountain View, California, the privately held company issues certain banking products via Evolve Bank & Trust.