Senior Research Infrastructure Engineer; ML Systems

Arta Finance | AI-Native Wealth Platform

Arta is on an audacious and incredibly rewarding mission: to pave the way for people everywhere to lead more successful financial lives. Arta leverages AI and sophisticated digital tools—once reserved for ultra-high-net-worth individuals—and makes them accessible to a broader global audience. Think of it as your own digital family office, combining intelligent investment strategies, alternative assets, private market access, and smart automation to help you grow and protect your wealth effortlessly.

We value trust, teamwork, and adaptability. Think: intelligent investing, personalized portfolios, and real-time trading, all backed by robust data infrastructure.

Arta is building an AI-native wealth management platform where machine learning systems directly power trading decisions, portfolio construction, and user intelligence.

Research Infrastructure is not a support function — it is the ML systems backbone of the company. You will design and scale the infrastructure that enables researchers and investment teams to train models, run large-scale experiments, simulate strategies, and deploy production trading systems reliably and reproducibly.

This role is for a senior individual contributor who enjoys owning complex distributed systems, cares about performance and correctness, and can translate cutting-edge ML research into hardened production systems.

If you like building ML platforms that actually move capital — not just publish benchmarks — this will be interesting.

• Architect and evolve large-scale distributed training and evaluation pipelines

• Build reproducible experimentation frameworks (data versioning, feature stores, experiment tracking, model registry)

• Design high-performance backtesting and simulation infrastructure for systematic trading strategies

• Enable seamless transition from research prototypes to production trading systems

• Develop infrastructure for signal generation, portfolio optimization, and execution workflows
  Build low-latency and batch processing pipelines for market, fundamental, and alternative datasets

• Partner with trading and research teams to product ionize alpha models and portfolio algorithms

• Improve throughput, latency, and reliability of compute-intensive workloads

• Ensure correctness, determinism, and auditability across research and trading systems

• Optimize distributed compute across cloud-native environments

• Improve orchestration of large-scale ML workloads

• Drive observability, monitoring, and failure isolation for ML and trading pipelines

• Write production-quality, well-tested code with a bias for simplicity and long-term maintainability

• Raise the engineering bar across research systems

• 5+ years of experience building production-grade distributed systems or ML infrastructure

• Deep experience designing large-scale data processing or training pipelines

• Strong background in ML systems, not just model development

• Proven ability to take ambiguous research requirements and turn them into scalable platforms

• Strong Python skills and fluency in modern ML ecosystems

• Experience operating high-compute workloads in cloud-native environments
  Comfortable owning complex systems end-to-end

You think in terms of system design, performance tradeoffs, and failure modes — not just scripts and notebooks.

• Experience building ML platforms at AI startups or research-driven tech companies
  Experience with systematic trading, quantitative research infrastructure, or portfolio optimization systems

• Experience with distributed training frameworks and large-model workflows

• Familiarity with high-performance computing or low-latency systems

A PhD is a plus, especially in:

• Computer Science (ML Systems, Distributed Systems, Systems for AI)

• Machine Learning / Artificial Intelligence
  Statistics or Applied Mathematics

• Operations Research (Optimization, Stochastic Systems)
  Computational Finance or Financial Engineering

• Econometrics

• Applied Physics (complex systems modeling)

We value deep technical training when it…