Machine Learning Engineer
Dallas, TX | Remote
This position is the architect of the Strike Global intelligence layer. As a Machine Learning Engineer, you will design and deploy the recursive models that power our real-time predictive engines. You will work at the frontier of high-frequency inference, transforming high-dimensional data streams into actionable execution signals.
We are seeking a specialist who understands that in our world, a model is only as good as its latency and its ability to maintain fidelity under extreme market pressure. You are not just building models; you are engineering the Continuum.
Responsibilities
Recursive Architecture: Design and implement deep learning and reinforcement learning models tailored for non-stationary, high-frequency environments.
Inference Optimization: Architect low-latency inference pipelines that ensure our models "Strike" with zero wasted motion. You will bridge the gap between high-level research and bare-metal execution.
Feature Engineering: Develop robust logic to extract 1-of-1 glints from raw telemetry and unstructured event data, ensuring the "Substrate" is fueled by the highest quality signal.
Model Sovereignty: Build the automated infrastructure for continuous training, validation, and deployment. You own the lifecycle of the intelligence, from the first gradient to production reality.
Systemic Hardening: Ensure the stability and interpretability of our ML systems, identifying and mitigating "drift" before it impacts the sovereign bottom line.
Skills Required
Algorithmic Mastery: Deep expertise in modern ML frameworks (PyTorch, JAX, or TensorFlow) and a visceral understanding of neural architecture search and optimization.
Technical Precision: Expert proficiency in Python and C++. You must be comfortable optimizing kernels and managing memory for high-throughput inference.
High-Dimensional Fluency: Proven experience handling large-scale, real-time data streams where the signal-to-noise ratio is razor-thin.
Architectural Autonomy: The ability to own the end-to-end ML stack without needing "Slouch" oversight. You are the final authority on the intelligence you build.
Rigorous Discipline: A commitment to the clinical validation of models. We value mathematical truth over heuristic guesses.