ML Engineer: Causal Inference & Predictive Modeling

Biorce; Austin (TX); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Hybrid work model

MacBook provided

Private health coverage

Pet-friendly office

Job summary

Biorce is seeking a Machine Learning Engineer to own causal inference and predictive modeling for a high-stakes AI engine in Austin. You will lead end-to-end work from research to production, designing causal and predictive models and applying interpretable techniques to ensure trust from stakeholders.

The role emphasizes autonomy, cross-functional collaboration with Product Owner, Designer, and Scientific Lead, and the drive to deliver benchmarked models within weeks.

Qualifications

• 5+ years of ML/AI experience.
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch).
• Solid experience with causal inference methods.
• Experience translating probabilistic predictions into decision-oriented outputs.
• Experience working with unstructured data signals for modeling pipelines.
• Knowledge of model interpretability techniques.
• Experience building production-grade ML systems.
• Ability to lead technical projects autonomously.
• Based in Austin or willing to relocate to a hybrid Austin office.

Responsibilities

• Own the full model lifecycle from research to production and monitoring.
• Work within the Sigma Squad with cross-functional teammates.
• Partner with data teams to extract signals from unstructured data.
• Design causal inference models from classical to deep learning-based approaches.
• Model outcomes under uncertainty and translate into expected-value estimates.
• Quantify confidence via uncertainty estimation methods.
• Write high-quality production Python code.
• Apply interpretability techniques to validate model behavior.
• Lead rigorous experimentation including causal validation and A/B tests.
• Collaborate with Product and Engineering to ship modeling solutions.
• Stay current with causal inference and probabilistic modeling advances.

Skills

ML/AI research

Python

Causal inference

Uncertain data

Interpretability

Production ML

Project leadership

Education

Master's or PhD in CS/Statistics

Tools

TensorFlow

PyTorch

Vertex AI

GCP

Job description

Biorce is seeking a Machine Learning Engineer to own causal inference and predictive modeling for a high-stakes AI engine in Austin. You will lead end-to-end work from research to production, designing causal and predictive models and applying interpretable techniques to ensure trust from stakeholders.

The role emphasizes autonomy, cross-functional collaboration with Product Owner, Designer, and Scientific Lead, and the drive to deliver benchmarked models within weeks.