- Company Name
- Evolve Group
- Job Title
- Machine Learning Researcher
- Job Description
-
**Job Title:** Machine Learning Researcher
**Role Summary:**
Design, develop, and deploy advanced ML models that generate real‑time trading signals in non‑stationary, adversarial market environments. Operate at the intersection of research and production, translating algorithmic concepts into low‑latency, robust decision systems that directly influence trading PnL.
**Expectations:**
- Own the full lifecycle: ideation → prototype → production deployment → continuous iteration.
- Deliver production‑ready models that meet stringent latency and reliability requirements.
- Collaborate closely with quants and engineers, merging domain expertise with cutting‑edge research.
- Scale solutions rapidly, leveraging available compute and data resources.
- Operate autonomously with minimal supervision, demonstrating initiative and responsibility.
**Key Responsibilities:**
1. Research and prototype high‑performance ML models (transformers, reinforcement learning, generative architectures) tailored for high‑frequency trading.
2. Build and maintain end‑to‑end pipelines, optimizing for low latency, high throughput, and fault tolerance.
3. Deploy models into live trading infrastructure; monitor real‑time performance, detect concept drift, and trigger retraining or re‑engineering cycles.
4. Conduct rigorous backtesting, statistical evaluation, and risk assessment to validate model impact on portfolio performance.
5. Interface with data engineering teams to ingest, preprocess, and manage massive proprietary time‑series datasets.
6. Document system designs, experiments, and results for internal and external stakeholders.
**Required Skills:**
- Deep expertise in machine learning and deep learning, including transformer, RL, and generative model domains.
- Proven experience deploying production ML systems (model serving, latency optimization, distributed training).
- Strong programming in Python, with familiarity in PyTorch/TensorFlow, CUDA, and GPU‑accelerated environments.
- Data engineering capabilities around large‑scale, high‑frequency time‑series data.
- Analytical mindset and self‑direction; ability to troubleshoot and solve complex problems independently.
- Knowledge of financial markets, trading mechanics, or quantitative finance is a plus but not mandatory.
**Required Education & Certifications:**
- PhD or Master’s in Computer Science, Machine Learning, Electrical Engineering, Applied Mathematics, or a related discipline.
- Demonstrated research output: publications in ML/AI conferences or journals is highly valued.
- No specific industry certifications required.