Senior Staff Engineer – Colorado Startup (Search Quality OR ML Model Engineering Expertise)

About the Opportunity

Join an elite, venture-backed team building next-generation, AI-powered collaboration tools for the enterprise.

Technical Integrity has been retained to lead the search for a rapidly growing startup developing AI systems that improve how large organizations think, communicate, and make decisions. Their product uses cutting-edge AI to identify and resolve coordination gaps automatically — helping teams operate more intelligently and efficiently at scale.

Following a recent and substantial round of funding, the company is expanding its world-class engineering team in Colorado and beyond.

Open Engineering Roles

• Staff Engineer (10+ years experience)
• Senior Staff Software Engineer (15+ years experience or high trajectory with 7+ years)
• Principal Software Engineer (20+ years experience)
• Infrastructure Specialist (Kubernetes, distributed systems, and cloud expertise)

Exceptional engineers with at least 7 years of experience and a record of rapid advancement are also encouraged to apply.

What You’ll Do

• Architect and implement core systems for an AI-powered enterprise collaboration platform.
• Define, design, and deliver innovative solutions in distributed systems, backend logic, and AI integration.
• Lead hands-on coding while setting technical direction across a small, senior team.
• Collaborate closely with product, design, and AI research peers to shape both strategy and implementation.
• Ensure infrastructure reliability, scalability, and security through modern cloud and Kubernetes technologies.
• Work with other Senior Staff Engineers to foster a culture of technical excellence, autonomy, and curiosity.

Required Experience - Search Quality Expertise OR ML Model Engineering

Search Quality

In this role, you will drive the development of high-quality search and retrieval systems that enable users to quickly access the most relevant information across complex, enterprise-scale datasets. You will design ranking algorithms grounded in modern information retrieval concepts, build semantic and keyword-based scoring models, and define the metrics that determine search relevance and quality. This includes developing both offline evaluation pipelines and online experimentation frameworks to measure improvements using metrics such as MRR, DCG/NDCG, and click-through rate. You’ll work closely with engineering and product partners to set quality benchmarks, tune ranking strategies, and ensure the system consistently delivers precise, contextually relevant results.

• Utilize advanced information retrieval concepts (such as MRR and IDF) to develop and refine ranking models that improve the quality and relevance of search results.​
• Collaborate across engineering and product teams to define ranking metrics, set quality benchmarks, and drive data-centric evaluations of search algorithms.​
• Design, develop, and implement algorithmic strategies for ranking, including keyword matching, semantic analysis, and machine learning-based scoring techniques.​
• Conduct offline and online experimentation (e.g., A/B tests) to assess improvements in search result quality using metrics like MRR, DCG, NDCG, and click-through rate.

ML Model Engineering

As an ML Model Engineer, you will design and optimize large language model–powered systems that support automated agent workflows. This includes building and refining LLM agents that can iteratively generate, evaluate, and improve prompts, as well as developing the underlying training loops, reward signals, and evaluation pipelines that enable autonomous prompt tuning at scale. You will apply advanced model optimization techniques — such as quantization, distillation, and low-rank adaptation — to improve inference performance and accelerate agent iteration cycles. This role sits at the intersection of systems engineering and modern LLM development, requiring deep experience with model efficiency, retrieval-augmented generation, and agentic architectures.

• Build and optimize LLM-powered systems that support autonomous agent workflows, including agents that iteratively generate, evaluate, and refine prompts.
• Develop training loops, evaluation pipelines, and reward mechanisms that enable automated prompt tuning and self-improving model behavior.
• Apply advanced model optimization techniques (e.g., quantization, pruning, distillation, LoRA/QLoRA) to improve model efficiency, reduce latency, and accelerate agent iteration cycles.
• Design and maintain scalable inference, retrieval, and vector-search components that support fast agent decision-making and prompt evaluation.

Ideal Candidate Profile

• Top 20% Engineer: Strong analytical ability, judgment, and breadth of experience. You think from first principles and have a track record of solving complex, real-world problems.
• Hands-On Builder: You’re happiest writing code and shipping production systems — not just managing others.
• Self-Directed & Autonomous: You thrive in a lightly structured environment, decomposing ambiguous problems and independently driving them to completion.
• Backend & Systems Focused: Most work centers on backend and business logic development, integrating with tools like Slack and Google Docs. Frontend experience is helpful but not central.
• Startup & Enterprise Experience: You balance startup agility with enterprise-scale thinking and rigor.
• Smart, Not Hard: You work efficiently and sustainably, typically producing exceptional results within a 45–50 hour workweek.
• AI & LLM Curiosity: You’re excited by building in an AI-native environment, leveraging Claude Code and other LLM-assisted tools to enhance development velocity and creativity.
• Collaborative & Solution-Oriented: You communicate thoughtfully, contribute positively to discussions, and pair every critique with a constructive solution.

How We Work

• We value leverage over brute force — working smart, not long hours. Engineers are trusted to define their own work, prioritize high-impact projects, and deliver results with minimal oversight.
• The team collaborates through lightweight, peer-driven conversations rather than rigid sprint cycles or daily standups. You’ll work alongside senior peers solving challenging systems problems in a small, high-trust team where every hire makes a visible impact.
• This is an environment for creative, disciplined problem solvers who thrive in freedom and accountability.

Technical Environment

Our stack and approach emphasize:

• AI-native development using Claude Code and LLM-assisted tools.
• Work at the bleeding edge of containerization, MCP, and distributed systems.
• A modern, cloud-first infrastructure built on Kubernetes.
• High-impact autonomy: engineers research, design, and deliver major systems end-to-end.

Interview Process

We’ve designed a focused process that balances technical rigor with respect for candidates’ time.

• Step 1: Introductory conversation with Technical Integrity.
• Step 2: Real-world coding exercise (typically ~4 hours).
• This is the main part of our interview process and replaces multiple rounds.
• It reflects our AI-native environment and requires use of Claude Code and modern containerization tools.
• Candidates often find it a valuable preview of what day-to-day work feels like here.
• Step 3: Three collaborative interviews with engineering leadership.

This process gives both sides a deep sense of technical fit and working style.

Compensation & Benefits

• Competitive salary and meaningful equity packages.
• Recent offers for comparable roles have ranged from $185,000–$295,000 base, plus signing bonuses and equity stakes.
• Flexible work arrangements — Colorado-based (preference for Denver/Boulder).
• Opportunity to collaborate with world-class technical peers on groundbreaking AI systems.

Application Process

To apply, please contact Technical Integrity with your resume and a concise statement of interest.

We value transparency, prompt feedback, and a respectful candidate experience throughout.

If you’re a senior software engineer or principal technologist who thrives on autonomy, deep technical challenges, and building at the frontier of AI-assisted engineering, we’d love to hear from you.