Machine Learning Engineer - Predictive Modeling, Causal Inference & Interpretability - Sigma Team - Austin

Biorce is a healthtech company using AI to support drug development and improve patient outcomes. The Machine Learning Engineer will lead causal inference and predictive modeling for an AI product that estimates clinical trial success, translates predictions into expected-value estimates, and explains the factors driving outcomes. The role spans research, prototyping, production deployment, monitoring, and collaboration with a cross-functional team.

Responsibilities

• Own the full lifecycle of projects leveraging deep learning and causal inference methods, from research to prototyping to final product and monitoring in production
• 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 with the data team to source, assemble, and extract usable signals from unstructured and semi-structured data for downstream modeling
• 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
• 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
• Use simulation-based or uncertainty quantification methods to gauge confidence in predictions and support robustness checks
• Develop and maintain high-quality, scalable, and robust code in Python
• Apply model interpretability techniques to validate model behavior and build stakeholder trust in predictions
• Conduct rigorous experimentation and model evaluation, including causal validation and A/B or quasi-experimental testing, to ensure performance and trustworthiness
• Collaborate with product and software engineering teams to integrate causal and predictive modeling solutions into our core products
• Stay current with the latest advancements in causal inference, deep learning, and applied statistics research

Skills

• You're a Machine Learning or AI Research Engineer with a genuine grounding in causal inference, not just predictive modeling, you can articulate the difference between "what predicts the outcome" and "what causes it," and you design accordingly
• You're comfortable turning probabilistic, uncertainty-laden predictions into expected-value estimates that a non-technical stakeholder can act on, and you don't treat interpretability as an afterthought, you build it in because the people relying on your models will immediately distrust anything that feels like a black box
• You're a relentless self-teacher who has picked up new tools, methods, and domains without waiting for a course or a manager to hand you one
• You're comfortable partnering with data teams to wring usable signal out of messy, unstructured data, and with scientists and researchers as collaborators rather than stakeholders
• You measure cycle time in weeks, communicate complex AI concepts to diverse audiences, and can lead a technical project autonomously from research question to production model
• 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
• Working knowledge of model interpretability and explainability techniques
• Solid experience architecting, developing, and deploying machine learning models, with a strong foundation in data structures, algorithms, and software engineering principles
• Ability to work autonomously, lead technical projects, and communicate complex AI concepts to diverse audiences
• Practical experience with deep causal inference approaches, Bayesian modeling, probabilistic programming, or simulation-based methods for uncertainty-aware prediction
• Familiarity with advanced or mechanistic interpretability methods, beyond standard attribution techniques
• Exposure to large language models and agentic AI systems, particularly for extracting structured signals from unstructured data or supporting causal/predictive workflows
• Master's or Ph.D. in Computer Science, Statistics, or a related technical field with a focus on causal inference or probabilistic modeling
• Contributions to research (publications in top-tier conferences) or open-source projects in causal inference, probabilistic modeling, or interpretability
• Experience with Google Cloud Platform and Vertex AI, or open-source causal ML and interpretability libraries
• Exposure to clinical research, life sciences, or another regulated domain
• Additional languages, we're an international team

Benefits

• Comprehensive private health coverage to ensure your physical and mental well-being.
• Hybrid work model offering flexibility to balance your professional and personal life.
• Company events to celebrate achievements and enjoy time together.
• Get equipped with a MacBook to enhance your productivity and work experience.
• Our office is pet-friendly!

Company Overview