Machine Learning Engineer

Location: Hybrid in Mountain View, US; in-office work required 2 days per week

Base salary: $187,000–$229,000 per year, plus equity and benefits

Company

hirify.global builds earned wage access and financial products that provide real-time financial flexibility without mandatory fees, interest rates, or credit checks.

What you will do

• Develop and train sequence, embedding, and classification models using large-scale financial and behavioral data.
• Build feature and data pipelines that produce training-ready datasets and maintain consistency between training and serving features.
• Design offline and online evaluation, including metrics, backtests, A/B tests, error tracing, and regression suites for ML models and agentic workflows.
• Deploy and operate models in production, including serving infrastructure, latency and cost optimization, retraining, and monitoring for drift and performance degradation.
• Fine-tune LLMs and build agentic orchestration with prompting, memory, context pipelines, retrieval, and tool integrations.
• Build Python backend services and RESTful APIs, instrument model and agent workflows, and collaborate with ML engineers, data scientists, and product teams.

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 skills and hands-on experience with PyTorch, NumPy, pandas, and scikit-learn.
• Knowledge of ML fundamentals, large-scale data processing with Spark, Databricks, or similar tools, and feature engineering on production data.
• Experience evaluating ML systems and LLM behavior, working with LLM APIs, prompt engineering, agentic frameworks, API design, asynchronous workflows, and SQL or NoSQL databases.
• Experience using AI-assisted development tools such as GitHub Copilot, Cursor, ChatGPT, or similar tools.

Nice to have

• LLM fine-tuning with Unsloth, Axolotl, LLaMA-Factory, HuggingFace PEFT/TRL, LoRA, or QLoRA.
• Distributed training, representation lhirify.globalg, MLOps tooling, vector stores, OpenTelemetry, container-based deployment, or serverless environments.
• Background in fintech, fraud, risk, or credit modeling.

Culture & Benefits

• Equity and employee benefits are included with the base salary.
• Work in a diverse and inclusive environment focused on belonging and representation.
• Contribute to financial products designed for people living paycheck to paycheck.