Top Remote Senior Level Machine Learning Engineer Jobs in CO

Lead architecture and technical vision for integrations that ingest external data (e.g., Salesforce, HubSpot) into Jasper's IQ layer. Design scalable, secure data ingestion, define context primitives, drive cross-team alignment, and deliver staff-level initiatives that improve reliability and AI-driven product quality.

Serve as the primary technical owner for customer engagements, design and build integrations, write production-quality code, troubleshoot and ship scalable production solutions, capture precise product feedback, and partner with product and engineering to convert field learnings into roadmap improvements.

Develop and optimize machine learning-based underwriting, settlement, and collection strategies across multiple consumer credit products. Build predictive models, decision systems, experimentation frameworks, and AI-assisted workflows using financial and behavioral data. Partner with Product, Engineering, Finance, Legal, and Compliance to improve portfolio performance, member outcomes, and responsible access to credit.

Lead technical direction for high-impact machine learning initiatives, from research and experimentation through production deployment. Build and fine-tune LLMs, document understanding systems, model-serving infrastructure, evaluation frameworks, and data feedback loops. Establish standards for model quality, calibration, reproducibility, and responsible deployment while mentoring ML engineers and influencing organizational roadmaps. The role requires hands-on software and ML engineering expertise, production-scale model development, and strong statistical judgment.

Designs, builds, and operationalizes production-grade generative AI and machine learning systems across AWS and GCP. Responsibilities include LLM applications, agentic workflows, RAG architectures, vector search, model lifecycle management, cloud-native microservices, responsible AI governance, HIPAA compliance, and optimization. The role also leads architecture, collaborates with product, security, data, and compliance teams, mentors engineers, and manages team performance.

Design, build, deploy, and optimize production ML and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, and chatbot architectures. Own technical delivery from architecture through monitoring and iteration, collaborate directly with enterprise clients, mentor junior ML engineers, and help establish internal technical standards and tooling.

Leads and coaches an AI engineering team while contributing hands-on to production systems. Responsibilities include translating product strategy into technical roadmaps, making architectural decisions for APIs and infrastructure, ensuring operational readiness and AI evaluation, improving testing and observability, and optimizing system efficiency and cost. The role partners across Product, Design, and Engineering to deliver impactful AI applications and integrations.

Design, develop, and deploy supervised machine learning models for credit decisioning. Collaborate with business and technical stakeholders to create data sources, improve modeling methodology, operationalize models, and manage model evaluation and risk for production systems.

Leads Tala’s Machine Learning Engineering team in building and operating platforms for model development, real-time inference, streaming features, batch processing, deployment, monitoring, and production reliability. Manages and develops 4–6 engineers, owns delivery and prioritization, provides architectural leadership, improves engineering practices and SLOs, and partners with Data Science, Data Engineering, Product, Credit, and Business Development teams.

Leads and develops a perception and machine learning engineering team building computer vision, sensor fusion, tracking, detection, and scene-understanding capabilities for autonomous systems. Owns roadmap planning, technical decisions, delivery, model development and deployment workflows, field readiness, hiring, performance management, and cross-functional execution with autonomy, embedded, data, hardware, product, programs, and field operations teams.