Systems & Research Engineer

About the Company

An applied AI lab, roughly two years old and recently seeded, building systems for the freight and supply chain industry: fraud and identity verification, high-volume voice and document processing, and carrier and customer communications. One flagship product already screens thousands of cases a day at industry-wide scale. The founding team brings elite technical backgrounds, including prior experience scaling a business to tens of billions of dollars in annual volume. The internal culture is flat (everyone holds the same working title regardless of seniority, intern to executive) and built around heavy daily use of coding agents, with engineers expected to operate with founder-level ownership across the stack.

About the Role

This is the systems and research counterpart to the team's applied engineering seat: internal-facing, focused on the infrastructure and research that applied engineers deploy to customers. A recent example of the kind of work involved: benchmarking multiple speech-to-text providers and building a hybrid open-source and production system that beat every vendor option on both cost and quality.

What You'll Own

• Performance and scale for the company's core systems (fraud detection, voice infrastructure, document ingestion), profiling, optimizing, and re-architecting as volume grows
• Structured research: form a hypothesis, design a benchmark, gather the data, and make a defensible call, for example evaluating and combining open-source versus closed-source model providers
• Distributed-systems tradeoffs at scale (concurrency, latency, cost) wherever models are being served
• Clear communication of findings and system constraints to the applied engineering team so they can plan around them
• Coding agents as the default way of working: your judgment goes toward what to build and verify, not manual execution

What We're Looking For

• Demonstrated performance engineering on an AI model-system mechanism: inference, serving, evaluation, retrieval, speech, or agent infrastructure. Distributed-systems work with no model in the loop does not qualify.
• Rigorous, scientific-method reasoning: can explain the hypothesis behind a technical decision, what alternatives were considered, and how bias in the data or benchmark was addressed
• Research that is genuinely on an AI system (model serving, benchmarking, or evaluation at scale), with real attention to concurrency, latency, and cost tradeoffs
• Fluent, heavy daily use of coding agents, assessed directly in the interview process
• Bonus: an AI/ML research background specifically, or a physics or other quantitative-science background moving into software

Logistics

Fully remote, open to any geography. There's no fixed working-hours or timezone overlap requirement, the team runs asynchronous, round-the-clock coverage globally, so strong written communication matters: the expectation is to post updates to a shared channel as soon as things happen, rather than sit on them until a meeting.

Compensation Note

$220K to $300K total comp (cash and equity)