Senior Research Infrastructure Engineer (ML Systems)

Arta Finance | AI-Native Wealth Platform
The Company
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.
The Role
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.
What You Will Do
Own the ML Systems Layer

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
Power AI-Driven Trading Infrastructure

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 productionize alpha models and portfolio algorithms
Improve throughput, latency, and reliability of compute-intensive workloads
Ensure correctness, determinism, and auditability across research and trading systems
Scale and Harden the Platform

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
Who You Are

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.
Strong Plus

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
PhD (Valued But Not Required)
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 translates into building robust, real-world systems.
What we offer

A competitive salary and benefits package, with ample opportunities for growth and advancement
A vibrant and dynamic work environment where innovation, collaboration, and continuous learning are highly valued
The opportunity to work with a diverse and talented team of industry experts, passionate about shaping the future of finance
Robust health insurance offering for you and your family
High deductible health plan available with health savings account contribution
20 weeks of parental leave
17 days PTO annually
Arta's Compensation Philosophy
We determine your salary based on factors including your interview performance, job-related skills, experience, and relevant education or training. Our offers are based on salary bands that are updated periodically using market benchmarks and consider geographic location as well (for example, higher cost regions like San Francisco or New York). If you are presented with an offer, we will review the base salary, benefits, number of options, notional option value and strike price. We would like to know if you accept our offer within 7 days.
Please keep in mind that the equity portion of your offer is not included in these numbers and represents a significant part of your total compensation.
IC I: $110,000-$180,000
IC II: $160,000-$230,000
IC III: $180,000-$300,000