Machine Learning Engineer

Deeter Analytics

9d • $150,000 - $200,000 • Remote

About The Position

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 market insights. This role involves building machine learning models to translate market insights into actionable trading strategies. The Machine Learning Engineer will be the hands-on ML person on a new research effort, taking ideas from concept to production. This includes data processing, model building, experimentation, and deployment into a daily running system that informs trading decisions. The role requires setting up and managing personal compute resources (GPU workstation or cloud instances) and owning the entire process from raw data to a usable result. This is an entry-level position with potential for growth, requiring strong fundamentals but no prior trading experience. The role is full-time, fully remote, and US-based.

Requirements

• Grounded in fundamentals: understanding of model internals (optimization, initialization, normalization, attention), mathematical underpinnings (linear algebra, probability, statistics), and the ability to derive gradients and analyze changes with batch size.
• Demonstrated ability as a builder through personal projects, hackathons, or models trained independently.
• Scrappy and hands-on approach, willing to set up infrastructure and fix issues independently.
• Honest about results, proactively seeking reasons for unexpected outcomes and prioritizing accuracy over personal results.
• Low ego and coachable, receptive to feedback and quick to adapt to new information.
• Curiosity about markets, with no prior trading experience required.
• Proficiency in Python and PyTorch, including idiomatic usage and first-principles implementation when necessary.
• Experience with NumPy and pandas for data manipulation.
• Understanding of time-ordered data, including non-leaking splits and backtest hygiene.
• Ability to reproduce research by reading papers and implementing them with one's own data.

Nice To Haves

• Fine-tuning or serving LLMs on personal hardware.
• Experience with CUDA or Triton.
• Experience with time-series forecasting.

Responsibilities

• Own the end-to-end process for a new research effort, from desk ideas and raw data to models, experiments, and actionable results.
• Build, train, ablate, and improve deep-learning models on market data, progressing from baseline models to daily operational systems.
• Set up and manage personal compute infrastructure (local GPU or AWS instances), including environment, drivers, containers, storage, experiment tracking, and cost control.
• Conduct leakage-proof validation on time-ordered data, perform regime-aware testing, and establish honest baselines to differentiate real results from noise.
• Read, reproduce, and summarize recent work in foundation models, time-series, and reinforcement learning for the team.
• Utilize modern AI tools to accelerate work in coding, literature review, and data wrangling, while verifying their outputs.

Benefits

• A seat inside a live trading operation, working directly with traders and researchers.
• A well-capitalized firm with a distinctive approach to markets.
• A deliberate growth path with increasing research agenda ownership.
• A small, low-ego, fully remote team.
• Compensation: $150k - $200k + bonus.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed