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Software Engineer

Location: Remote (Chile) Seniority: Junior (0–2 yrs) · Mid-Level (3–5 yrs) · Senior (6+ yrs) Type: Full-time

About TruckerCloud

TruckerCloud is the leading telematics data platform for commercial auto insurance, unifying data from hundreds of telematics and camera providers into a single, easy-to-use solution. Our mission is to help insurers and risk teams streamline telematics programs, improve underwriting accuracy, enhance claims workflows, and leverage real-time and historical fleet insights—all without the technical overhead of custom integrations. With connections to 100+ telematics systems, deep analytics, automated reporting, and powerful behavioral insights, TruckerCloud enables data-driven decision-making and scalable risk management across the transportation ecosystem.

About the Role

TruckerCloud is seeking Software Engineers who thrive in an end-to-end engineering culture. Engineers here own the full lifecycle of their work—from requirements and architecture to implementation, automated testing, deployment, monitoring, and iteration.

You will work on cloud-first systems that support rapidly expanding data volumes and exponentially growing workloads. Our platform already handles hundreds of terabytes of data, and this scale is increasing aggressively every quarter.

This is an AI-first engineering team. A large part of the work we are doing today depends on strong, practical expertise with AI—both to accelerate how we build and to power the capabilities we ship to customers. We also expect solid knowledge of security and compliance best practices, given the enterprise and insurance context we operate in.

Tech Stack You'll Work With

• Backend: Java (primary), Python
• Frontend: React
• Cloud: AWS & GCP
• Databases: MySQL/Aurora, BigQuery
• AI: Coding assistants and agents, LLM APIs, prompt and context engineering, retrieval-augmented generation, evaluation frameworks
• Scale: Systems handling hundreds of TB with exponential growth

How We Work with AI

AI proficiency is a core requirement at every level, not a bonus. Across the team, we expect you to:

• Use AI tools daily for coding acceleration, architecture exploration, troubleshooting, test generation, and documentation.
• Write clear, well-scoped prompts and provide the right context to get reliable results.
• Critically review every AI-generated output—you own the code you ship, regardless of what produced the first draft.
• Follow our policies for handling customer and sensitive data when using AI tooling.

What scales by level: junior engineers are expected to use AI effectively and validate it; mid-level engineers build reusable AI workflows and ship LLM-powered product features; senior engineers architect those features end-to-end—retrieval and context design, agentic workflows, guardrails, evaluations, and the cost/latency/privacy trade-offs behind them—and define how the whole team works with AI.

What You Will Do

• Work with product and internal stakeholders to define requirements and design scalable solutions.
• Develop and maintain backend services in Java and complementary components in Python.
• Build, maintain, and optimize APIs, distributed components, and integrations.
• Contribute to frontend features in React when the work requires it.
• Implement unit, integration, and automated tests—we do not rely on dedicated QA roles.
• Build AI-assisted and AI-powered capabilities, including the evaluation logic that proves they work.
• Participate in deployments, release processes, observability, and performance tuning.
• Follow and enforce security and compliance standards (access management, logging, auditability, secure coding).
• Improve engineering practices, architecture, and internal documentation.

What scales by level: the complexity and independence of the work. Junior engineers execute well-defined pieces with support; mid-level engineers own features end-to-end; senior engineers lead architecture, mentor others, and drive engineering standards across the team.

What We're Looking For

Required at every level:

• Solid programming fundamentals: data structures, version control, testing, debugging.
• Working knowledge of Java and Python.
• SQL and understanding of relational databases (MySQL/Aurora).
• Familiarity with AWS or GCP.
• Demonstrable, hands-on use of AI tools in real engineering work.
• Awareness of security best practices and secure coding.
• Experience with (or genuine interest in) CI/CD and automated testing.
• Ownership-driven mindset and excellent written communication for a fully remote team.
• Professional working proficiency in English.

What scales by level:

• Depth in Java — from working knowledge (junior) to deep expertise, including performance and design trade-offs (senior).
• Distributed systems and cloud architecture — from familiarity with concepts to proven track record designing systems at scale.
• Large-scale data processing — from basic exposure to operating pipelines and stores at hundreds of TB.
• AI expertise — from effective tool usage to shipping and evaluating production LLM features.
• Leadership — from active learning to mentorship and technical ownership of an area.

Nice to Have (all levels)

• Experience with data engineering: ETL/ELT pipelines, BigQuery or other analytical data systems, data quality and validation frameworks.
• Experience collaborating with Data Scientists to productionize models and data-driven features.
• Exposure to Docker, Kubernetes, serverless architectures, or infrastructure-as-code.
• Orchestration tools such as Airflow.
• Cloud cost optimization experience.
• Background in telematics, insurance, IoT, or logistics.

Hiring Process

1. Application review — CV and profile screening.
2. Screening conversation — we align on your experience, expectations, and the level that fits best.
3. Technical interview(s) — one or two rounds depending on the level. Junior candidates typically have a single technical interview focused on fundamentals, coding, and how you work with AI tools. Mid-level and senior candidates typically have a second round covering system design, architecture trade-offs, and production ownership—scaled to the level.
4. Cultural interview — how you collaborate, take ownership, and communicate in a fully remote, end-to-end engineering team.
5. Conversation with our CEO — optional, for senior profiles. A chance to discuss our direction and where your work fits into it.

The number of steps varies with seniority, so we will confirm your specific process during the screening conversation.

Use of AI in the Application Review Process

We use AI tools to organize, summarize, and rank applications against the requirements above. No application is rejected automatically — every decision is made by a person.