Job description

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Smartsheet is building the next generation of AI-powered strategic planning and work execution agents through SmartAssist, our intelligent agent platform. You will work closely with Engineering and AI Platform teams, embedded in a team working in a critical area and building toward a mature Agent Development Lifecycle (ADLC). You will architect how we design, build, and operationalize AI-driven solutions that transform work execution at enterprise scale. You Will: Design and build production-grade LLM-powered agents and workflows within Smartsheet, including architecture decisions on system design, data pipelines, and deployment strategies Develop and optimize prompts, RAG pipelines, and agent reasoning patterns; build evaluation frameworks to measure accuracy, hallucination rates, and performance across model versions Implement and manage end-to-end AI systems on cloud infrastructure (AWS, GCP, or Azure), including monitoring, optimization, and incident response Collaborate with Engineering, Product, and cross-functional teams to translate business requirements into technical AI solutions that drive real impact Mentor and guide other engineers on applied ML best practices, modern AI development patterns, and production considerations Establish best practices and standards for AI development, model governance, and responsible AI within the organization Stay current with the rapidly evolving AI/LLM landscape and evaluate emerging tools, frameworks, and techniques for Smartsheet's needs Contribute to a full stack application– both legacy and greenfield You Have: 8+ years of software engineering experience, with at least 2 years working directly with LLMs in production Deep, hands-on experience with prompt engineering and context engineering, you understand how model behavior changes with framing, structure, and input design Strong working knowledge of RAG architectures: chunking strategies, embedding models, retrieval evaluation, and failure diagnosis Experience building or extending LLM evaluation frameworks, you have designed scorers, worked with golden datasets, and thought carefully about what good looks like Fluency in agent system design, you don't need to own the architecture, but you can engage as a peer on architectural tradeoffs that affect quality Strong Python skills; comfortable working in data-heavy environments (Databricks, Delta tables, or equivalent) Ability to communicate complex quality findings (written and verbal) to both technical and non-technical stakeholders, you can explain what’s broke, why it matters, and what needs to happen next without losing the room Strong cross-functional judgment, you know when to escalate, when to resolve independently, and how to build credibility across engineering, product, and AI platform teams A bias for clarity in ambiguous situations, when failure modes are murky and trade-offs are real, you bring structure and a clear point of view rather than waiting for consensus Legally eligible to work in the U.S. on an ongoing basis BS or MS in Computer Science, a related field, or equivalent industry experience Strong Plus: Experience with MLflow or similar experiment tracking platforms Familiarity with CI-integrated evaluation pipelines Experience with multi-agent orchestration frameworks Prior work in an Applied AI or LLMOps function within a product company What Success Looks Like: Delivered measurable, validated quality improvement on at least one SmartAssist agent across our defined quality dimensions Expanded evaluation coverage to close the most significant blind spots in our current framework Established a repeatable quality improvement methodology the broader team can apply Current US Perks & Benefits: Employer subsidized medical/vision and dental coverage for full-time employees 401k Match to help you save for your future (50% of your contribution up to the first 6% of your eligible pay) Monthly stipend to support your work and productivity Flexible Time Away Program, plus Sick Time Off US employees are automatically covered under Smartsheet-sponsored life insurance, short-term, and long-term disability plans US employees receive 12 paid holidays per year Up to 24 weeks of Parental Leave Personal paid Volunteer Day to support our community Opportunities for professional growth and development including access to Udemy online courses Company Funded Perks, including a counseling membership, local retail discounts, and your own personal Smartsheet account Teleworking options from any registered location in the U.S. (role specific) Smartsheet provides a competitive base salary range for roles that may be hired in different geographic areas we are licensed to operate our business from. Actual compensation is determined by several factors including, but not limited to, level of professional, educational experience, skills, and specific candidate location. In addition, this role will be eligible for a market competitive incentive opportunity. US Base Salary Pay Range $175,000 — $245,000 USD Get to Know Us: At Smartsheet, your ideas are heard, your potential is supported, and your contributions have real impact. You’ll have the freedom to explore, push boundaries, and grow beyond your role. We welcome diverse perspectives and nontraditional paths—because we know that impact comes from individuals who care deeply and challenge thoughtfully. When you’re doing work that stretches you, excites you, and connects you to something bigger, that’s magic at work. Let’s build what’s next, together. Equal Opportunity Employer: Smartsheet is an Equal Opportunity (EEO) employer committed to fostering an inclusive environment with the best employees. It is our policy to provide equal employment opportunities to all qualified applicants in accordance with applicable laws in the US, UK, Australia, Germany, Costa Rica, Japan, Bulgaria, India, and Singapore. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information. If there are preparations we can make to help ensure you have a comfortable and positive interview experience, please let us know. #LI-Remote

Requirements

Must-haves

You will architect how we design, build, and operationalize AI-driven solutions that transform work execution at enterprise scale.
Design and build production-grade LLM-powered agents and workflows within Smartsheet, including architecture decisions on system design, data pipelines, and deployment strategies
Develop and optimize prompts, RAG pipelines, and agent reasoning patterns; build evaluation frameworks to measure accuracy, hallucination rates, and performance across model versions
Implement and manage end-to-end AI systems on cloud infrastructure (AWS, GCP, or Azure), including monitoring, optimization, and incident response
Collaborate with Engineering, Product, and cross-functional teams to translate business requirements into technical AI solutions that drive real impact
Mentor and guide other engineers on applied ML best practices, modern AI development patterns, and production considerations
Establish best practices and standards for AI development, model governance, and responsible AI within the organization
Stay current with the rapidly evolving AI/LLM landscape and evaluate emerging tools, frameworks, and techniques for Smartsheet's needs
Contribute to a full stack application– both legacy and greenfield
8+ years of software engineering experience, with at least 2 years working directly with LLMs in production
Deep, hands-on experience with prompt engineering and context engineering, you understand how model behavior changes with framing, structure, and input design
Strong working knowledge of RAG architectures: chunking strategies, embedding models, retrieval evaluation, and failure diagnosis
Experience building or extending LLM evaluation frameworks, you have designed scorers, worked with golden datasets, and thought carefully about what good looks like
Fluency in agent system design, you don't need to own the architecture, but you can engage as a peer on architectural tradeoffs that affect quality
Strong Python skills; comfortable working in data-heavy environments (Databricks, Delta tables, or equivalent)
Ability to communicate complex quality findings (written and verbal) to both technical and non-technical stakeholders, you can explain what’s broke, why it matters, and what needs to happen next without losing the room
Strong cross-functional judgment, you know when to escalate, when to resolve independently, and how to build credibility across engineering, product, and AI platform teams
A bias for clarity in ambiguous situations, when failure modes are murky and trade-offs are real, you bring structure and a clear point of view rather than waiting for consensus
Legally eligible to work in the U.S. on an ongoing basis
BS or MS in Computer Science, a related field, or equivalent industry experience
Experience with MLflow or similar experiment tracking platforms
Familiarity with CI-integrated evaluation pipelines
Experience with multi-agent orchestration frameworks
Prior work in an Applied AI or LLMOps function within a product company

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmawsgcpazuremlpython

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Parental Leave