Founding Software Engineer – Machine Learning Systems & Infrastructure

About MarketCrunch AI

We’re reimagining how retail investors access and act on investment research. MarketCrunch AI delivers predictive, AI-powered stock insights with the same clarity and confidence usually reserved for hedge funds. Our models combine macro data, price action, and sentiment, delivering clean, explainable signals to thousands of users.

Founded by experts from Meta, Apple, Pinterest, Amazon, as well as Wharton and CMU, we’re building the future of AI-powered investing—accessible to everyone.

The Role

As a Founding Software Engineer focused on ML Systems & Infrastructure, you’ll help scale our prediction engine and research stack. You'll own the systems that run and serve dozens of deep learning models across asset classes—powering both our product and internal research pipelines.

You’ll work closely with machine learning researchers, frontend engineers, and product teams to take ideas from prototype to production. Your work will directly impact how our multi-agent workflows reason, trigger alerts, and deliver insights to users.

Responsibilities

• Design and scale robust systems for training, serving, and monitoring ML models
• Productionize experimental models across asset classes with a focus on throughput, latency, and reliability
• Support multi-agent orchestration, allowing prediction modules to interact with APIs, databases, and user triggers
• Maintain infrastructure for backtesting, feature stores, model versioning, and statistical evaluation
• Build APIs and backend services that power our frontend apps and internal tools
• Collaborate cross-functionally with ML, frontend, and infra teams to enable seamless model deployment and usage

Job Requirements

• 2–4+ years of experience in backend or infrastructure roles, ideally at high-growth or data-driven companies
• Strong coding skills in Python (FastAPI, Pandas, NumPy) and familiarity with JavaScript
• Experience with cloud ML pipelines (GCP, AWS), especially using tools like Airflow, MLflow, Ray, Docker, and Kubernetes
• Deep understanding of model lifecycle: training, hyperparameter tuning, evaluation, deployment, and monitoring
• Solid grasp of probability, statistics, and time-series modeling
• Familiarity with PostgreSQL, Redis, and high-throughput data systems
• Ability to read ML code and collaborate deeply with research teams
• Experience with LLMs, multi-agent systems, or tools like LangChain, RAG, or vector DBs

Nice-to-Haves

• Strong debugging skills and ability to optimize for model latency and memory footprint
• Experience shipping Python packages, managing CI/CD, and enforcing testing coverage
• Prior experience deploying explainable AI systems or user-facing AI features
• Comfort working with ambiguity and rapidly evolving requirements in a startup setting
• Experience with backtesting frameworks, quant research environments, or real-time trading infrastructure

Why Join Us

• Tackle challenging technical problems at the intersection of AI, finance, and UX
• Be part of a tight-knit team building proprietary infrastructure used by thousands
• Work with high agency, ownership, and visibility across the product
• Backed by leading advisors (e.g., Wharton’s MSQF faculty director) and pre-seed investors
• Flexible remote work and opportunity for significant equity upside