Software Engineer, Data Acquisition

You'll own the systems that feed Nascent's trading and research operations with the data they need to compete - building and operating the web scraping and data acquisition infrastructure that runs 24/7 across a heterogeneous fleet of machines, providers, and operating systems. This is a roughly 50/50 split between software engineering and operations: half your time you're writing production code (primarily Rust) to build new scrapers, defeat anti-bot countermeasures, and architect resilient pipelines;

the other half you're deep in logs, monitoring dashboards, and fleet health - diagnosing failures, tuning proxies, and keeping a distributed system humming across mixed infrastructure. You'll work closely with analysts and researchers who depend on the data you deliver. The problems are genuinely interesting: every major website is actively trying to stop you, infrastructure spans multiple cloud providers and bare metal, and the surface area of what breaks is enormous.

This role is remote-first. Montreal proximity is preferred - the team has a growing analyst presence there and in-person collaboration matters - but it's not a hard requirement. North American or European time zones work best for team overlap.

• Build and maintain production-grade web scraping systems - primarily in Rust - designing scrapers that are resilient to site changes, rate limiting, CAPTCHAs, and evolving anti-bot countermeasures.
• Operate and monitor a heterogeneous distributed infrastructure spanning multiple cloud providers, bare metal, and mixed operating systems - you own uptime, not just deployments.
• Diagnose production issues from raw logs and telemetry, building observability into every system you ship so problems surface before they cascade.
• Design and implement proxy management, rotation strategies, and network-layer evasion techniques to maintain reliable data acquisition at scale.
• Develop tooling for fleet health monitoring, automated alerting, and self-healing infrastructure across a diverse set of machines and hosting environments.
• Collaborate with analysts and researchers to understand data requirements, prioritize new source integrations, and ensure data quality and freshness meet trading-grade standards.
• Reverse-engineer web applications and APIs - inspecting network traffic, deobfuscating JavaScript, and adapting to adversarial changes in target sites.
• Continuously improve system reliability, throughput, and maintainability - refactoring scraping pipelines, optimizing connection handling, and reducing operational toil through automation.
• Deploy and maintain agentic workflows for data source discovery, onboarding, and troubleshooting - using LLM-based agents to automate the identification of new sources, accelerate integration, and surface and resolve failures in existing pipelines.

You are a builder who also runs what you build - you don't consider a project done when the PR merges; you consider it done when it's been stable in production for weeks. You have a genuine interest in the cat-and-mouse game of web scraping at scale: anti-bot systems, browser fingerprinting, proxy rotation, and the constant adaptation it requires. You are comfortable navigating messy, heterogeneous infrastructure - mixed OS environments, multiple hosting providers, hardware you didn't provision - and you make it better over time.

You are energized by operational puzzles: tracing a failure across distributed logs, identifying a subtle network degradation, or figuring out why a scraper that worked yesterday is now blocked. You write clean, production-grade code and care about systems that run unattended and fail gracefully. You are proficient in at least one major programming language (Go, Python, C++, Java, or similar) - the stack is primarily Rust, and prior Rust experience is a strong plus, but if you haven't written it professionally, you're the kind of engineer who picks up new languages fast and takes ownership of the ramp.

Python is useful for scripting, prototyping, and analyst-facing tooling. You thrive in less-structured environments where you're trusted to prioritize your own work, and you take ownership of outcomes rather than waiting for tickets. You are fluent with agentic workflows and LLM-based tooling - you know how to design and operate AI agents to discover new data sources, automate onboarding, and triage failures in production pipelines.

You don't treat AI as a novelty; you reach for it when it's the right…