Machine Learning Engineer, Earnin
$187-229k
+ Equity
2 days a week in office (Mountain View, CA)
Paycheck advance service for employees
Posted today
Paycheck advance service for employees
501-1000 employees
Posted today
$187-229k
+ Equity
2 days a week in office (Mountain View, CA)
501-1000 employees
Company mission
To build a financial system that works for people.
Role
Who you are
- 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
What the job involves
- 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
- 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
Our take
Paying bills is an inevitable part of life, but paying them late or dipping into overdrafts can result in fees. This is the reality for millions of workers who have to wait for their paychecks to arrive – and the problem that companies like EarnIn are trying to solve. By allowing hourly workers access to their pay as it is earned rather than on a fixed date, it hopes to provide all Americans with the ability to deal with immediate costs without going into arrears.
Unlike competitors in this growing space, Earnin charges no mandatory fees, instead gaining revenue through a voluntary tipping system. It also requires no credit checks to access its services, which puts it ahead of the growing number of digital banking solutions. This approach has garnered it a strong user base of business customers who offer Earnin as an employee benefit.
Earnin has managed to continue its steady growth even while the wider tech landscape suffered setbacks, which suggests a wise leadership. It also continues to improve its offering and expand its network of businesses, aiming to give every employee in America control of the money they've earned.
Freddie
Company Specialist at Welcome to the Jungle
Insights
42% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
Dec 2018
$125m
SERIES C
Sep 2017
$39m
SERIES B
Total funding: $190.1m
Company benefits
- 401k
- FSA
- Mental health resources
- Employee Resource Groups
- Stipend for growth opportunities
- Pet friendly office
- Work from home opportunities
- Health insurance
Company HQ
Evergreen Park, Palo Alto, CA
Leadership
Ram Palaniappan
(Founder & CEO)Was Senior Operations Manager at Reflect.com, a P&G company. Served as President of UniRush from 2004 to 2012.
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