ML Engineer: Causal Inference & Predictive Modeling

Biorce; Austin (TX); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Hybrid work model

Private health coverage

MacBook provided

Pet-friendly office

Company events

Job summary

Biorce is seeking a Machine Learning Engineer for the Sigma Team in Austin to own causal inference and predictive modeling for our AI engine predicting clinical trial success. You will lead end-to-end development from research to production, specializing in interpretable, decision-oriented outputs built on uncertain data.

You will collaborate with a Product Owner, Designer, and embedded Scientific Lead to ship a benchmarked model within weeks, applying interpretability techniques and robust

Qualifications

• 5+ years of experience in Machine Learning, Deep Learning, or AI Research.
• Proven proficiency in Python and ML/Deep Learning frameworks (TensorFlow, PyTorch, etc.).
• Solid, hands-on experience with causal inference methods.
• Experience modeling and predicting outcomes from uncertain, noisy, or incomplete data, and translating probabilistic predictions into expected-value or decision-oriented outputs.
• Experience working with data teams to extract structured signals from unstructured or messy data sources for use in modeling pipelines.

Responsibilities

• Own the Model Lifecycle: Lead the full lifecycle of projects leveraging deep learning and causal inference methods, from research to prototyping to final product and monitoring in production.
• Build Inside the Sigma Squad: Work embedded with a Product Owner, a Designer, and the Scientific Lead from day one, taking causal and predictive models from an ambiguous problem to a demoable, benchmarked product in weeks rather than quarters.
• Partner on Data Extraction: Partner with the data team to source, assemble, and extract usable signals from unstructured and semi-structured data for downstream modeling.
• Design Causal Inference Models: Design causal inference models, from classical to deep learning-based methods, to answer counterfactual questions and distinguish what actually drives outcomes from what merely correlates with them.
• Model Outcomes Under Uncertainty: Build models that predict the likelihood of event success under uncertain, noisy, or incomplete data, translating predictions into expected-value estimates to support decision-making.

Skills

Python

Machine Learning

Deep Learning

Causal Inference

Communication

Education

Master’s or Ph.D. in Computer Science, Statistics, or related technical field

Tools

TensorFlow

PyTorch

Vertex AI

Google Cloud Platform

Job description

Biorce is seeking a Machine Learning Engineer for the Sigma Team in Austin to own causal inference and predictive modeling for our AI engine predicting clinical trial success. You will lead end-to-end development from research to production, specializing in interpretable, decision-oriented outputs built on uncertain data.

You will collaborate with a Product Owner, Designer, and embedded Scientific Lead to ship a benchmarked model within weeks, applying interpretability techniques and robust