Quantitative Researcher

Quantitative Researcher | World Models & Quantitative Perception About Astera Astera is building decision intelligence for events across markets. Our systems transform noisy real-world events into structured, actionable intelligence across sports, prediction markets, macro, crypto, and equities. We are building toward generalized world models capable of understanding dynamic environments, extracting latent structure from partially observed systems, and improving decision quality under uncertainty. We are pursuing problems at the intersection of: multimodal reasoning quantitative inference agentic systems event-driven intelligence architectures The Role We are hiring a Research Engineer focused on world models, quantitative perception systems, and latent-state reasoning. You will work on systems that: model dynamic environments in latent space extract actionable signal from noisy multimodal data quantify qualitative phenomena into structured representations usable by downstream agents and decision systems This role sits between: applied research quantitative modeling reinforcement learning systems engineering You should be comfortable operating in ambiguous, frontier-style research environments with a high degree of autonomy. Responsibilities Develop latent-space and world-model architectures for dynamic real-world systems Build models that infer hidden state from noisy or partially observed environments Design quantitative frameworks for extracting signal from high-dimensional data Research and implement multimodal reasoning systems across vision, temporal, and structured data Build spatiotemporal perception and forecasting pipelines Develop representation-learning systems for event understanding and state estimation Design agent memory and long-horizon reasoning mechanisms Build research-grade experimentation, evaluation, and simulation frameworks Collaborate with infrastructure, AI, and product teams to productionize research systems Qualifications Strong background in machine learning, applied mathematics, computer science, physics, quantitative research, or a related technical field Experience building ML systems in Python using PyTorch, JAX, or TensorFlow Strong understanding of probabilistic reasoning and statistical inference Experience working with noisy, high-dimensional, or partially observed datasetsExperience working with noisy, high-dimensional, or partially observed datasets let's fix. But wait, the list ends prematurely due to mis-structure. Let's adjust. Continuing the list. Ability to independently design and run research experiments Strong systems-thinking and problem-solving ability Preferred Experience predictive world models sequence modeling memory architectures reinforcement learning trajectory modeling agent-based systems Computer Vision & Perception object tracking spatiotemporal forecasting vision transformers sports tracking sensor fusion systems Quantitative Signal Extraction extracting signal from noisy environments identifying weak predictive structure Physics-Based & Causal Modeling dynamical systems state transition modeling Technical Stack We value strong engineering fundamentals more than specific tools, but experience with the following is highly relevant: Python PyTorch JAX TensorFlow C++ CUDA OpenCV RL frameworks Distributed training systems Scientific computing libraries Time-series and probabilistic modeling frameworks What We Look For High intellectual rigor Strong research intuition Systems-level thinking Comfort operating under ambiguity Curiosity across domains Bias toward truth-seeking over consensus Ability to extract structure from disorder We are specifically interested in people capable of quantifying the qualitative. Nice-to-Have Backgrounds Autonomous systems Robotics Quantitative trading Scientific computing Aerospace / space systems Sports analytics Knowledge graphs Agentic systems Real-time inference systems High-performance ML infrastructure Compensation Meaningful equity participation Opportunity to work on frontier-scale problems with a highly technical team #J-18808-Ljbffr Austincc