Machine Learning Engineer

📋 Job Overview

Deeter Analytics is a privately held investment research and trading firm managing its own capital across public markets. After years of discretionary success, we think we have some unique ways of seeing the market, and we pair sharp human judgment with modern AI to act on them. We're hiring a Machine Learning Engineer to build models that can be turned into trading strategies. This is a hands-on role where you'll take an idea from the desk and turn it into data, models, and experiments, then carry it through to something that runs every day and informs how we trade.

🏢 About Deeter Analytics

Deeter Analytics is a privately held investment research and trading firm managing its own capital across public markets. We pair sharp human judgment with modern AI to act on unique ways of seeing the market. We're looking for talented individuals to join our team and help us build the next generation of trading strategies.

🎯 The Role

Reporting to the senior researcher who leads the project, you'll be the hands-on ML person on a new research effort: take an idea from the desk and turn it into data, models, and experiments, then carry it through to something that runs every day and informs how we trade. You'll set up and run your own compute (a GPU workstation or a few cloud instances, not a cluster) and own the whole loop from raw data to a result the team can act on. It's an entry point into a serious seat: no trading experience needed, deep fundamentals required, and the role grows with you as the work proves out. The role is full-time and fully remote, US-based.

✅ Key Responsibilities

• Own a new research effort with a senior lead; from a desk idea and raw data to models, experiments, and a result the team can act on.
• Build, train, ablate, and improve deep-learning models on market data, from the first baseline to something that runs every day.
• Set up and run your own small GPU setup, local or on AWS: environment, drivers, containers, storage, experiment tracking, cost.
• Perform leakage-proof validation on time-ordered data, regime-aware testing, and honest baselines.
• Read recent work in foundation models, time-series, and RL: reproduce what matters and write up what you found in a page the team will act on.
• Use modern AI tools to move faster (code, literature, data wrangling) and check their work.

📌 Required Qualifications

• Grounded in fundamentals: knowledge of what's inside the models you train (optimization, initialization, normalization, attention, why a run diverges or plateaus) and the math underneath: linear algebra, probability, statistics.
• A builder: personal projects, hackathon builds, a model trained on your own machine, a repo people actually use. You've shipped things nobody assigned you.
• Scrappy and hands-on: You'd rather stand up the box, fix the CUDA driver, and get the first experiment running tonight than wait for someone to provision it.
• Honest about results: You go looking for the reason your number is too good before anyone else does, and you'd rather kill your own result than have the market do it.
• Low ego and coachable: You take feedback well, update quickly when the facts change, and care more about the answer than the credit.
• Curious about markets, not credentialed in them: Interest helps; experience isn't required; the lead has that, and we'll teach you the domain.

⭐ Desirable Experience

• Fine-tuning or serving LLMs on your own hardware.
• CUDA or Triton experience.
• Time-series forecasting experience.

🎁 Benefits

• A seat inside a live trading operation, working directly with the traders and researchers who act on your models.
• A well-capitalized firm with a distinctive approach to markets.
• A deliberate growth path: own one project end to end first, then take on more of the research agenda as you prove out.
• A small, low-ego, fully remote team.
• Compensation: $150k - $200k + bonus.

🛂 Visa & Eligibility

US-based role. Eligibility to work in the United States is required.