Quantitative Researcher

Austin Community College; New York (NY); On-site; USD 100,000 - 140,000; Full time

Benefits offered by this job

Meaningful equity participation

Opportunity to work on frontier-scale problems

Job summary

Astera is seeking a Research Engineer to focus on world models and quantitative perception systems. This role entails the development of architectures for dynamic real-world systems and building models that extract actionable signals from data.

The ideal candidate should possess a strong technical background in machine learning and programming with experience using Python, PyTorch, and TensorFlow. You will collaborate with technical teams to deploy innovative research solutions.

Qualifications

• Experience building ML systems in Python using PyTorch, JAX, or TensorFlow.
• Strong understanding of probabilistic reasoning and statistical inference.
• Ability to independently design and run research experiments.

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.
• Collaborate with infrastructure, AI, and product teams to productionize research systems.

Skills

Machine learning

Applied mathematics

Systems thinking

Problem-solving

Education

Strong background in a technical field

Tools

Python

PyTorch

JAX

TensorFlow

C++

Job description

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
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• 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