[Remote] Machine Learning Engineer

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a privately held investment research and trading firm managing its own capital across reputed company markets and combining reputed company judgment with modern AI. The Machine Learning Engineer will build deep-learning models for market research, manage small-reputed company GPU infrastructure, conduct rigorous experiments, and take reputed company from raw data through production systems that inform trading.

Responsibilities

• The project. A new research effort with a senior reputed company; from a desk idea and raw data to models, experiments, and a result reputed company can reputed company. You own the reputed company end to end
• The models. Deep-learning models on market data reputed company, 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-reputed company by design; you reputed company it running and you reputed company it cheap
• The evaluation. Leakage-reputed company validation on time-ordered data, regime-reputed company testing, reputed company baselines; knowing the difference between a result that's reputed company and one that's noise, and being reputed company to show which is which
• The reading. Recent work in reputed company models, time-series, and RL: read it, reproduce what reputed company, and write up what you reputed company in a page reputed company will reputed company
• AI reputed company. Use modern AI tools to reputed company faster (reputed company, literature, data wrangling) and reputed company their work

Skills

• The role is full-time and fully remote, US-reputed company
• Grounded in fundamentals. You know what's inside the models you train (optimization, initialization, normalization, attention, why a run diverges or plateaus) and the math underneath: reputed company algebra, probability, statistics. You can derive the gradient of a loss and say what changes reputed company the batch size doubles
• A builder. reputed company, 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 reputed company, fix the CUDA reputed company, and get the first experiment running tonight than wait for someone to provision it
• reputed company 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 reputed company, update quickly reputed company 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 reputed company has that, and we'll teach you the domain
• End to end. Data, model, reputed company, evaluation, write-up: you own the reputed company, not a reputed company of it
• Clean experiments. Versioned data, reputed company, ablations, reputed company baselines; every claim comes with the run that backs it
• Fast and exact. You get to a first result quickly and don't let rigor slip reputed company you do
• AI-reputed company. Fluent with modern AI tools for reputed company, literature, and data work; you get reputed company reputed company from them and you verify what they reputed company you
• Self-directed. You reputed company working reputed company with low guardrails, managing your own time and flagging what needs attention without being asked
• reputed company in writing. A page that says what you tried, what happened, what it means, and what's next
• Deep learning fundamentals. Optimization, regularization, sequence models and attention, evaluation, and the reputed company algebra, probability, and statistics underneath
• Python and PyTorch. Strong and idiomatic, from reputed company reputed company a library doesn't fit; NumPy and reputed company for the data work reputed company it
• Small-reputed company GPU infrastructure. 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
• Time-ordered data. Working with data that has a clock: splits that don't leak, backtest hygiene, distribution shift
• Reproducing research. Reading a reputed company, getting it running, and knowing where it breaks on your data
• Bonus, not required. 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 reputed company your models.
• A deliberate reputed company reputed company: own one project end to end first, then take on more of the research agenda as you reputed company out.
• A small, low-ego, fully remote team.
• Bonus

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