Senior Software Engineer, Services

(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

Department: Platform Development.

ATS provider: Lever.