Product-Driven ML Engineer for Production SaaS

WorkWave; United States; Remote; USD 160,000 - 170,000; Full time

Benefits offered by this job

Remote-first culture

Flexible time off

Tuition reimbursement

Health benefits

Job summary

WorkWave is seeking a product-minded Applied Data Scientist or ML Engineer to build, ship, and scale ML-powered products that help customers make decisions, operate their businesses, and serve their own users.

This role requires taking machine learning from problem definition through experimentation, production deployment, measurement, iteration, and long-term ownership. You will bridge data engineering, ML Ops, and applied data science to bring AI to life in a SaaS environment.

Qualifications

• 3+ years (ideally 5+) of professional experience in applied data science, machine learning, or ML engineering, including hands-on production models.
• Strong Python skills and hands-on experience with ML libraries and frameworks (Scikit-Learn, XGBoost, PyTorch, TensorFlow).
• Solid SQL expertise is required.

Responsibilities

• End-to-End ML Ownership: Drive ML capabilities powering customer-facing SaaS products.
• Pipeline & model development: design data/feature pipelines from discovery to deployment and monitoring.
• Product integration: embed ML into product workflows and user experiences.
• Pragmatic prototyping: move from prototype to production balancing accuracy and latency.

Skills

Python

Scikit-learn

XGBoost

PyTorch

TensorFlow

SQL

ML Ops

Tools

Airflow

dbt

Dagster

Snowflake

BigQuery

Redshift

Databricks

Job description

WorkWave is seeking a product-minded Applied Data Scientist or ML Engineer to build, ship, and scale ML-powered products that help customers make decisions, operate their businesses, and serve their own users.

This role requires taking machine learning from problem definition through experimentation, production deployment, measurement, iteration, and long-term ownership. You will bridge data engineering, ML Ops, and applied data science to bring AI to life in a SaaS environment.