Sepal AI builds the world’s hardest AI benchmarks—grounded in real-world enterprise and operational systems. We work with experienced professionals to develop high-fidelity simulations and benchmarks that challenge the capabilities of frontier AI models.
About the role
Sepal AI builds the world’s hardest AI benchmarks—grounded in real-world enterprise and operational systems. We work with experienced professionals to develop high-fidelity simulations and benchmarks that challenge the capabilities of frontier AI models.
We’re hiring a Machine Learning Engineer to help us design and validate realistic ML evaluations. that reflect the complexity of enterprise-grade systems. You'll work on short-term engagements to solve applied ML problems, assess solution quality, and contribute to AI research by grounding benchmark tasks in practical, real-world use cases.
RESPONSIBILITIES
-Develop and validate machine learning solutions based on real-world enterprise problems
-Translate ambiguous business goals into well-defined ML tasks and workflows
-Evaluate data quality, model performance, and implementation feasibility
-Document decisions, trade-offs, and key assumptions clearly and concisely
-Collaborate asynchronously with internal teams to align on solution quality and technical expectations
MINIMUM QUALIFICATIONS
-English fluency
-Legally eligible to work and located in the U.S.
-Graduate degree (Master's or higher) in Computer Science, Data Science, Statistics, or a related field
-Minimum of 5 years of professional experience in machine learning, data science, or applied analytics
-Available for 10–20 hours per week
-Reliable internet connection and access to a laptop or desktop computer
-Comfortable using collaborative tools (e.g., Google Docs, GitHub, Slack, video conferencing)
-Strong ability to work independently, manage time effectively, and follow written instructions
EXPERIENCE QUALIFICATIONS
-Demonstrated experience building ML models using real-world data in a production or enterprise setting
-Skilled in Python and core ML/data libraries (e.g., pandas, scikit-learn, XGBoost, TensorFlow or similar)
-Adept at assessing the strengths and limitations of ML approaches in business contexts …
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