Senior Infrastructure Engineer
Storm2
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Mountain View, CA
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Mid-Senior level
Posted March 13, 2026
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Responsibilities
Responsibilities
- Backtest and simulate systematic strategies
- Move research prototypes into stable production systems You’ll own the ML systems layer end-to-end.
- Architecting distributed training and evaluation pipelines
- Building reproducible experimentation frameworks (data versioning, model registry, tracking)
- Designing high-performance backtesting and simulation systems
- Supporting signal generation, portfolio optimisation, and execution workflows
- Scaling compute across cloud-native environments
- Improving throughput, latency, and reliability
- Ensuring determinism, correctness, and auditability across research and trading systems You’ll operate across ML systems, distributed compute, and trading infrastructure.
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Requirements
- Building reproducible experimentation frameworks (data versioning, model registry, tracking)
- Designing high-performance backtesting and simulation systems
- Scaling compute across cloud-native environments
- Ensuring determinism, correctness, and auditability across research and trading systems You’ll operate across ML systems, distributed compute, and trading infrastructure.
- 5+ years building production-grade distributed systems or ML infrastructure
- Experience designing large-scale data or training pipelines
- Strong Python skills within modern ML ecosystems
- Ability to translate research requirements into scalable systems
- Clear thinking around system design, failure modes, and performance trade-offs
- Comfortable owning complex systems end-to-end Strong Plus
- Experience in systematic trading or quant research infrastructure
- Experience with distributed training frameworks
- Familiarity with high-performance or low-latency systems Why Apply?
- Infrastructure that directly supports live trading
- Senior IC ownership with real technical influence
- Research-led engineering environment
Preferred Skills
- Comfortable owning complex systems end-to-end Strong Plus
- Experience in systematic trading or quant research infrastructure
- Experience with distributed training frameworks
- Familiarity with high-performance or low-latency systems Why Apply?
- Infrastructure that directly supports live trading
- Senior IC ownership with real technical influence
- Research-led engineering environment
- Complex systems with meaningful performance requirements This role suits engineers who think in terms of system design and long-term architecture.
⚡ Senior ML Infrastructure Engineer 📍 Mountain View (3 days per week in office) 💲 Competitive base + equity I’m supporting our client, a well-capitalised AI-native wealth platform, as they hire a Senior ML/Research Systems Engineer to own the research infrastructure powering live trading and portfolio systems. This is a senior IC position focused on building and scaling distributed ML systems that directly support trading decisions and capital allocation. If you like building platforms that are used in production and held to a high bar for performance and correctness, this will be relevant. The Opportunity You’ll design and scale infrastructure that enables research and trading teams to:
Train and evaluate models at scale
Run reproducible large-scale experiments
Backtest and simulate systematic strategies
Move research prototypes into stable production systems You’ll own the ML systems layer end-to-end. What You’ll Work On
Architecting distributed training and evaluation pipelines
Building reproducible experimentation frameworks (data versioning, model registry, tracking)
Designing high-performance backtesting and simulation systems
Supporting signal generation, portfolio optimisation, and execution workflows
Scaling compute across cloud-native environments
Improving throughput, latency, and reliability
Ensuring determinism, correctness, and auditability across research and trading systems You’ll operate across ML systems, distributed compute, and trading infrastructure. What They’re Looking For
5+ years building production-grade distributed systems or ML infrastructure
Experience designing large-scale data or training pipelines
Strong Python skills within modern ML ecosystems
Ability to translate research requirements into scalable systems
Clear thinking around system design, failure modes, and performance trade-offs
Comfortable owning complex systems end-to-end Strong Plus
Experience in systematic trading or quant research infrastructure
Experience with distributed training frameworks
Familiarity with high-performance or low-latency systems Why Apply?
Infrastructure that directly supports live trading
Senior IC ownership with real technical influence
Research-led engineering environment
Complex systems with meaningful performance requirements This role suits engineers who think in terms of system design and long-term architecture. Interested in applying? Click Easy Apply or email me at ben.watts@storm2.com ⚡Storm2 is a specialist FinTech recruitment firm with clients across Europe, APAC, and North America. To discuss open opportunities or career options, visit storm2.com and follow the Storm2 LinkedIn page for the latest roles and market intel.
Train and evaluate models at scale
Run reproducible large-scale experiments
Backtest and simulate systematic strategies
Move research prototypes into stable production systems You’ll own the ML systems layer end-to-end. What You’ll Work On
Architecting distributed training and evaluation pipelines
Building reproducible experimentation frameworks (data versioning, model registry, tracking)
Designing high-performance backtesting and simulation systems
Supporting signal generation, portfolio optimisation, and execution workflows
Scaling compute across cloud-native environments
Improving throughput, latency, and reliability
Ensuring determinism, correctness, and auditability across research and trading systems You’ll operate across ML systems, distributed compute, and trading infrastructure. What They’re Looking For
5+ years building production-grade distributed systems or ML infrastructure
Experience designing large-scale data or training pipelines
Strong Python skills within modern ML ecosystems
Ability to translate research requirements into scalable systems
Clear thinking around system design, failure modes, and performance trade-offs
Comfortable owning complex systems end-to-end Strong Plus
Experience in systematic trading or quant research infrastructure
Experience with distributed training frameworks
Familiarity with high-performance or low-latency systems Why Apply?
Infrastructure that directly supports live trading
Senior IC ownership with real technical influence
Research-led engineering environment
Complex systems with meaningful performance requirements This role suits engineers who think in terms of system design and long-term architecture. Interested in applying? Click Easy Apply or email me at ben.watts@storm2.com ⚡Storm2 is a specialist FinTech recruitment firm with clients across Europe, APAC, and North America. To discuss open opportunities or career options, visit storm2.com and follow the Storm2 LinkedIn page for the latest roles and market intel.