Staff Software Engineer

Cast & Crew

About The Position

At Cast & Crew, we’ve empowered creativity and supported the global entertainment industry for decades. Together with our family of brands - Backstage, CAPS, Checks & Balances, Final Draft, Media Services, Sargent-Disc, and The TEAM Companies – we operate as a combined entertainment technology and services provider offering industry standard screenwriting accounting software, digital payroll products, data & reporting, and a host of creative tools. The industry continues to move faster than ever, and the need for our expertise, our technology, and our people has never been greater. We are a production’s best ally every step of the way. #OneCastOneCrew We are looking for a Staff Software Engineer to serve as a technical anchor and force multiplier for our engineering teams. In this role, you will shape architecture, drive modernization, and cultivate a culture of engineering excellence — all while keeping a pragmatic eye on product delivery. You will bridge the gap between high-level strategy and day-to-day execution, ensuring that every iteration moves us meaningfully closer to our long-term engineering goals.

Requirements

7+ years of professional software engineering experience, including hands-on work with both strongly-typed and dynamically typed languages.
Proficiency across multiple technology stacks with a "T-shaped" profile — broad knowledge of modern engineering disciplines paired with deep expertise in key areas.
Demonstrated ability to modernize legacy applications and resolve complex architectural challenges.
Strong command of Domain-Driven Design (DDD) principles and software engineering design patterns.
Expertise in monolith decomposition strategies that maintain roadmap alignment and minimize disruption.
Well-versed in DevOps practices and quality engineering disciplines (CI/CD, testing strategies, observability, etc.).
Hands-on experience building MCP servers and/or AI skills, with a strong understanding of agentic workflow patterns and context management strategies.
Experience contributing to AI tooling — such as AI-assisted code review systems, memory/context retention utilities, or developer productivity infrastructure built around LLMs.
Familiarity with applying AI to quality engineering and customer analytics (e.g. test generation, usage pattern analysis, behavioral insights).
Familiarity with AI-assisted development tools (e.g. GitHub Copilot, Cursor, Claude Code) and a track record of driving their adoption within an engineering team.
Awareness of AI governance principles and the ability to apply organizational AI standards in day-to-day engineering practice.
A genuine enthusiasm for AI-driven development culture — someone who leads by example and helps others grow their AI fluency.
Proven ability to think strategically while executing tactically and operationally.
Skilled communicator with experience presenting to mixed technical and non-technical audiences.

Responsibilities

Serve as "Anchor" (team lead) for one or more engineering teams, providing technical direction and accountability.
Develop architecture and strategy for features and projects, ensuring alignment with broader modernization goals.
Lead the adoption of new technologies while maintaining architectural cohesion across the platform.
Guide tech-debt elimination in parallel with feature development, balancing long-term health with near-term roadmap commitments.
Evaluate multiple approaches to complex problems and champion optimal, evidence-based solutions.
Champion the team's adoption of AI-driven development practices, serving as the primary advocate and practitioner on the ground.
Design and build Model Context Protocol (MCP) servers and AI skills that extend and integrate AI capabilities into the team's workflows and products.
Contribute to the broader AI tooling ecosystem, including AI-driven peer review pipelines, context retention mechanisms, prompt and memory management utilities, and other infrastructure that improves the efficiency and reliability of AI-assisted workflows.
Monitor and assess AI-driven development progress across the team, identifying opportunities to accelerate adoption and address gaps.
Apply AI to quality engineering — leveraging it to enhance test coverage, automate regression analysis, and surface risk earlier in the development cycle.
Use AI to analyze customer usage patterns, surfacing insights that inform product decisions and help the team build with greater empathy and precision.
Contribute to and help shape the company's AI-driven engineering culture — modeling best practices, sharing learnings broadly, and influencing how AI is adopted across teams beyond your own.
Mentor team members on effective, responsible use of AI tooling — from code generation to agentic workflows — helping them build fluency and confidence.
Adhere to and operationalize guidelines established by the AI Center of Excellence, ensuring the team's AI practices remain consistent, secure, and aligned with organizational standards.
Evaluate emerging AI development tools and frameworks, providing informed recommendations on what to adopt, pilot, or defer.
Establish and model best practices in code quality, testing, DevOps, and software design.
Leverage peer review processes as a lever to raise the quality bar across the team.
Quantify engineering excellence and provide teams with clear capability roadmaps.
Stay current with industry trends and inspire the team to explore and adopt better solutions.
Drive deep understanding in technical discussions — going beyond surface-level involvement to uncover root causes and durable solutions.
Present complex technical concepts clearly to audiences with varying levels of expertise, from engineers to product and business stakeholders.
Understand customer motivations, needs, and frustrations within the context of both the technology and the broader industry.
Balance a commitment to technical excellence with pragmatic product roadmap goals.
Provide mentorship to engineers at all levels, fostering continuous growth for both individuals and the team as a whole.
Champion a culture of analytical and evidence-based decision making.