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

Biorce is a pioneering Healthtech company dedicated to revolutionizing drug development through the power of AI. We are passionate about accelerating medical advancements and improving patient outcomes.

Our team comprises seasoned clinical research professionals, data scientists, and AI experts, working collaboratively to bridge the gap between cutting-edge technology and real-world clinical needs.

With an unwavering commitment to revolutionize healthcare, we envision a world where all patients benefit from accelerated and cost-effective access to treatments. Biorce is poised to redefine the landscape of healthcare, shaping a future where innovation and accessibility converge for the betterment of humanity.

Biorce is a pioneering Healthtech company dedicated to revolutionizing drug development through the power of AI. We are passionate about accelerating medical advancements and improving patient outcomes.

Our team comprises seasoned clinical research professionals, data scientists, and AI experts, working collaboratively to bridge the gap between cutting-edge technology and real-world clinical needs.

With an unwavering commitment to revolutionize healthcare, we envision a world where all patients benefit from accelerated and cost-effective access to treatments. Biorce is poised to redefine the landscape of healthcare, shaping a future where innovation and accessibility converge for the betterment of humanity.

We're looking for a Machine Learning Engineer to join the Sigma Team in Austin and own the causal inference and predictive modeling core of one of our most strategically critical products: an AI engine that predicts the likelihood of clinical trial success, turns that prediction into a decision-oriented, expected-value estimate, and tells sponsors what is actually driving the outcome rather than what merely correlates with it.

You’ll lead the full lifecycle of this work from research through prototyping to a monitored production model, designing causal inference approaches (classical through deep learning-based) to answer counterfactual questions, building predictive models that hold up under uncertain, noisy, or incomplete data, and applying interpretability techniques so the people betting multi-million-dollar development decisions on your model’s output can trust it. You’ll work embedded in a small, cross-functional Sigma squad alongside a Product Owner, a Product Designer, and an embedded Scientific Lead, moving from an ambiguous problem to a demoable, benchmarked model in weeks rather than quarters.

Sigma sits inside Biorce’s CSO’s Office. It’s Biorce’s Tech Special Forces, an incubator tasked with building the most disruptive, highest-stakes AI products in the clinical trial space.

This is not a team that iterates on existing products. Sigma builds from zero: defining the strategy, shipping under real speed and ambiguity, and treating every initiative as a new venture. Squads are small and cross-functional, a Product Owner, AI/ML Engineers, an embedded Scientific Lead, and a Designer, all in the room from day one, not brought in once the problem is already framed.

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…