Sales Engineer

Factor Funding Company

. Lead technical discovery calls with prospects, surfacing requirements, constraints, and success criteria across data engineering, ML, data science, analytics, and AI engineering use cases.

Posted 10/31/2026full-timeRemote • ColombiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Machine Learning System Design, including the ability to architect and defend end-to-end ML/AI systems. Possesses strong client-facing skills and advanced English proficiency to effectively communicate technical concepts to diverse stakeholders.

Highest-signal resume keywords

Machine Learning System DesignGenAI/LLM Systems ExpertiseData Engineering PipelinesClient-Facing Presentation SkillsAdvanced English Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning EngineeringAI EngineeringData EngineeringEnd-to-End MLOpsRAG ArchitecturesAgentic WorkflowsModel Serving InfrastructureEvaluation FrameworksPrompt EngineeringContext Engineering

Soft Skills

Exceptional Written CommunicationVerbal CommunicationStructured ThinkingProblem DecompositionClient Listening Skills

Tools & Technologies

Data WarehousesData LakehousesFeature StoresAnalytics Workflows

Industry Keywords

Technical Discovery CallsTechnical AuthorityTechnical ProposalsProof-of-Concept PlansFast-Paced Environments

About the role

Key responsibilities & impact

Lead technical discovery calls with prospects, surfacing requirements, constraints, and success criteria across data engineering, ML, data science, analytics, and AI engineering use cases.
Design and present ML, AI, and Data Science system architectures live with clients, including RAG, agentic workflows, LLM evaluation pipelines, model serving infrastructure, and end-to-end MLOps.
Translate ambiguous business problems into scoped, deliverable engagements by defining solution approaches, identifying risks, sizing effort, and shaping statements of work alongside Account Executives.
Serve as the deal-side technical authority, defending architectural choices and challenging suboptimal designs.
Build technical proposals, reference architectures, and proof-of-concept plans.
Partner with delivery leadership to hand off won deals, ensuring scope, assumptions, and technical context transfer accurately.
Develop and maintain reusable solution patterns, demos, and reference implementations.
Stay current on the AI/ML landscape and apply that knowledge in client conversations.
Provide feedback to product, delivery, and marketing on prospect needs and offering improvements.
Represent Factored externally through webinars, technical content, conference conversations, and select customer events.

Requirements

What you’ll need

5+ years of total professional engineering experience, with a clear track record of increasing technical responsibility and scope.
3+ years working hands-on as a Senior Engineer in AI Engineering, Machine Learning Engineering, Data Engineering or an adjacent production-focused role.
Demonstrable expertise in machine learning system design: ability to whiteboard end-to-end ML/AI systems live, reason about trade-offs (latency, cost, accuracy, maintainability), and defend choices under technical scrutiny.
Deep practical experience with GenAI/LLM systems: RAG architectures, agentic patterns, evaluation frameworks, prompt and context engineering, fine-tuning approaches, and inference infrastructure.
Working fluency across the broader data stack: data engineering pipelines (batch and streaming), warehouses/lakehouses, feature stores, and analytics workflows — enough to credibly scope work outside pure AI/ML.
Advanced English proficiency, with exceptional written and verbal communication — capable of holding the room with both senior technical stakeholders and executive buyers.
Strong client-facing instincts: comfortable presenting, listening for the real problem under the stated one, and converting ambiguity into a concrete plan.
Track record of execution in fast-paced, ambiguous environments — owns outcomes end-to-end without needing tight supervision.
Structured thinking under time pressure: can decompose a half-formed client problem into a clean architecture in a 60-minute call.

Benefits

Comp & perks

Ownership through equity participation.
Annual company retreat.
Education bonus for continuous learning.
Company-wide winter break.
Paid time off.
Optional in-person events and meetups.
Tailored career roadmaps.
High-performance culture.