Senior Software Engineer – AI Infrastructure
Payward, Inc. is the parent company and operating platform behind Kraken and a portfolio spanning trading, custody, payments, lending, tokenized assets, onchain finance, and benchmarks.
Maintainer signals as of 9/25/2026
Funding history
Investors
Projects
About Payward, Inc.
Payward operates unified financial infrastructure across crypto and traditional markets, including shared liquidity, risk and margin systems, collateral and settlement, compliance, and licensing. Its first-party portfolio includes Kraken, Kraken Pro, NinjaTrader, Breakout, xStocks, Payward Services, CF Benchmarks, Reap, Krak, Kraken Prime, Kraken OTC, Kraken Custody, and Bitnomial.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Design and build production infrastructure for AI agent systems, including high-performance Rust services for model inference, orchestration, and execution; scalable ML infrastructure and MLOps patterns; guardrails and observability; and reliable systems serving millions of users.
Requirements
- 5+ years of experience building and operating high-scale production systems
- Strong proficiency in Rust and systems-level programming
- Deep understanding of distributed systems, reliability engineering, and performance optimization
- Experience operating services serving millions of users or high-throughput workloads
- Familiarity with ML infrastructure, model serving, or MLOps in production environments
- Experience designing observability, monitoring, and failure recovery systems
- Strong collaboration skills across infrastructure and applied engineering teams
- High ownership mindset in a high-stakes production environment
Responsibilities
- Design and build infrastructure for AI agent systems in production
- Develop high-performance Rust services for model inference, orchestration, and execution
- Architect scalable systems supporting millions of users and high request throughput
- Build ML infrastructure and MLOps patterns for model deployment, evaluation, and monitoring
- Define guardrails, observability, and failure handling for agent-driven workflows
- Optimize latency, throughput, and cost across inference and orchestration layers
- Translate experimental prototypes into hardened production systems
- Contribute to foundational infrastructure decisions in a high-scale, high-impact environment
Hiring Process
Candidates may be asked to complete job-related skills or work-style assessments. Results are considered alongside experience and interviews and are not the sole basis for an employment decision.