or apply directly on EarnIn's site. We never take the application ourselves.
Is this posting real?
- This role has been open
- 8 days EarnIn's roles stay open a median of 45 days
- Reposted
- No
- Salary listed
- Yes 59% of EarnIn's roles list one
- Ghost-job risk at EarnIn
- high 19 stale, 1 reposted of 32 open
- Hiring momentum
- 51 roles opened in the last 90 days ↑ up vs. the prior 90 days
- Last confirmed on the employer's board
- 2026-09-17
Measured from postings appearing on and disappearing from EarnIn's own greenhouse board since 2026-08-03. Full hiring picture for EarnIn.
About this role
As a Machine Learning Engineer at EarnIn, you will be responsible for developing, training, and deploying machine learning models that enhance user-facing financial products. Your role involves building data pipelines, designing evaluation metrics, taking models to production, and collaborating with cross-functional teams to create intelligent AI features. You will also work with large-scale financial data and fine-tune large language models for internal applications.
- benefits
- 2/5
- freshness
- 5/5
- career value
- 4/5
- role clarity
- 5/5
- pay transparency
- 5/5
Scored from the posting itself — how clearly the role is described, how much it says about pay and benefits, and how recently it was listed. Not a judgement of EarnIn as an employer.
What you need
- 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
Nice to have
- Experience with LLM fine-tuning using frameworks such as Unsloth, Axolotl, LLaMA-Factory, or HuggingFace PEFT/TRL, including parameter-efficient methods (LoRA/QLoRA)
- Experience with distributed training or representation learning
- Familiarity with MLOps tooling for experiment tracking, feature stores, or model registries (MLflow, Weights & Biases, Feast)
- Familiarity with vector stores (e.g., Weaviate, Pinecone, Qdrant)
- Knowledge of OpenTelemetry or similar observability frameworks
What you get
- Base salary range of $187,000–$229,000
- Equity
- Benefits
Worth weighing
- Hybrid position requiring in-office work 2 days a week
- Salary range provided but no specific benefits detailed beyond equity and general benefits
- The role involves working with large-scale financial data which may require strong data handling skills
Summarised from EarnIn's posting. Read the full original.
Listed by EarnIn on their greenhouse job board, last confirmed open on 2026-09-17. PitchMeAI is not the employer.
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