Note: The job is a remote job and is open to candidates in USA. Deeter Analytics is a privately held investment research and trading firm that applies modern AI to public-market investing. The Machine Learning Engineer will lead an early-career research effort end to end, converting market ideas and raw data into deep-learning models, experiments, evaluations, and production systems that inform trading.
Responsibilities
- The project. 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. You own the loop end to end
- The models. Deep-learning models on market data built, trained, ablated, and improved by you, from the first baseline to something that runs every day
- The compute. Your own small GPU setup, local or on AWS: environment, drivers, containers, storage, experiment tracking, cost. Small-scale by design; you keep it running and you keep it cheap
- The evaluation. Leakage-proof validation on time-ordered data, regime-aware testing, honest baselines; knowing the difference between a result that's real and one that's noise, and being able to show which is which
- The reading. Recent work in foundation models, time-series, and RL: read it, reproduce what matters, and write up what you found in a page the team will act on
- AI leverage. Use modern AI tools to move faster (code, literature, data wrangling) and check their work
Skills
- The role is full-time and fully remote, US-based
- You know 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
- You can derive the gradient of a loss and say what changes when the batch size doubles
- Personal projects, hackathon builds, a model trained on your own machine, a repo people actually use. You've shipped things nobody assigned you
- 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
- 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
- You take feedback well, update quickly when the facts change, and care more about the answer than the credit
- Interest helps; experience isn't required; the lead has that, and we'll teach you the domain
- Data, model, infra, evaluation, write-up: you own the loop, not a slice of it
- Versioned data, seeds, ablations, honest baselines; every claim comes with the run that backs it
- You get to a first result quickly and don't let rigor slip when you do
- Fluent with modern AI tools for code, literature, and data work; you get real leverage from them and you verify what they give you
- You thrive working remotely with low guardrails, managing your own time and flagging what needs attention without being asked
- A page that says what you tried, what happened, what it means, and what's next
- Optimization, regularization, sequence models and attention, evaluation, and the linear algebra, probability, and statistics underneath
- Strong and idiomatic, from first principles when a library doesn't fit; NumPy and pandas for the data work around it
- Setting up and running your own training and inference environment on a local machine or a few AWS instances: CUDA, containers, storage, monitoring, cost control
- Working with data that has a clock: splits that don't leak, backtest hygiene, distribution shift
- Reading a paper, getting it running, and knowing where it breaks on your data
- Fine-tuning or serving LLMs on your own hardware; CUDA or Triton; time-series forecasting
Benefits
- A seat inside a live trading operation, working directly with the traders and researchers who act on your models.
- 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.
- Bonus
Company Overview