(This role is US based remote and all candidates must be able to work in the US now and in the future without restriction)
What we believe
Technology is no longer just an enabler of business strategy. It is the business strategy. And with AI reshaping how companies operate, compete and grow — while rapidly accelerating the pace of change — the stakes have never been higher.
Our role
We’re focused on helping private equity investors and portfolio company leaders drive value creation through technology. In a world where investors deploy trillions annually into software and tech-enabled businesses, we're the team that makes sure the underlying technology actually delivers.
Our approach
The expert judgment of our experienced practitioners is complemented by proven frameworks, tech-enabled solutions, and objective data to help organizations navigate critical technology decisions across diligence, transformation, growth, cybersecurity, AI, and operational execution.
What we value
Everything we do is grounded in five core values: Service. Curiosity. Credibility. Commitment. Creativity.
If you're energized by solving complex technology challenges and helping others succeed in critical moments, you'll fit right in.
Job Role
We’re building small, highly capable engineering pods (2–3 engineers) that own problems end-to-end and move quickly from idea to production. This role is for engineers who are comfortable operating across the stack while bringing strong backend and systems design expertise.
This role is part of our Services team, which builds the shared backend services and system foundations that power many of Crosslake’s tools and products. These systems are designed for reuse, scalability, and long-term maintainability.
This role requires strong architectural thinking and hands-on execution, with a focus on building reliable, extensible services that support multiple use cases over time.
How we work
- Small teams, high ownership, minimal handoffs
- Fast, iterative delivery with an emphasis on sound system design
- Pragmatic decision-making over over-engineering
- Engineers are expected to operate across the stack, not within silos
Key Responsibilities
- Design and build shared backend services and APIs used across multiple systems
- Define service boundaries, contracts, and data models
- Own the full SDLC: design, development, testing, deployment, and iteration
- Contribute to domain modeling and system design decisions
- Ensure services are scalable, reliable, and reusable
- Improve system cohesion and reduce duplication across tools and products
- Use AI tools to accelerate development while maintaining high code quality
- Balance speed with long-term maintainability and extensibility
Requirements
- 7+ years of software engineering experience
- Strong backend expertise with the ability to operate across the full stack when needed
- Experience designing service-oriented or distributed systems
- Familiarity with domain-driven design (DDD) principles
- Exceptional systems thinking and ability to model complex domains
- Experience deploying and operating applications in cloud environments (AWS, Azure, or GCP)
- Solid understanding of the full SDLC
- Deep experience with (i.e., daily usage of) AI coding tools
AI-Native Development
- Hands-on experience using AI-assisted development tools beyond basic code generation
- Ability to leverage AI across the workflow (e.g., prototyping, debugging, test generation, QA, code review, security analysis)
- Ability to balance AI-assisted development with sound system design and long-term maintainability
- Familiarity with modern AI-enabled development environments and practices
Preferred Experience
- Infrastructure as Code (e.g., Terraform)
- CI/CD and modern DevOps practices
- API design (REST, GraphQL, event-driven systems)
- Messaging systems (e.g., Kafka, SQS), Event sourcing, CQRS patterns
- Data modeling (SQL and NoSQL)
- Observability practices (logging, metrics, tracing)
- Experience with one or more of the following languages: Python, TypeScript, Golang, Rust
- Basic understanding of data engineering principles
What success looks like
- You design services that are reused across multiple systems
- You create clean abstractions that simplify downstream development
- You balance speed with long-term maintainability
- You improve system consistency and reduce fragmentation
- You effectively use AI tools to increase speed without sacrificing quality
