Applied ML Engineer – Model R&D

Nascent Markets

. Refactor research-quality and prototype ML code into production-ready, maintainable, deployable systems .

Posted 11/11/2026full-timeRemote • United States, CanadaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying and maintaining production-ready ML systems, with a strong focus on Rust proficiency, CI/CD practices, and collaborative engineering. Capable of integrating structured development methodologies while addressing architectural quality and reliability in ML pipelines.

Highest-signal resume keywords

Rust ProficiencyCI/CD PracticesML Pipeline DeploymentNLP ExperienceMLOps Tooling Familiarity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel TrainingFeature EngineeringModel ServingMonitoringLoggingAlerting InfrastructureTask ManagementGit WorkflowsCode Reviews

Soft Skills

CollaborationCuriosityAutonomy

Tools & Technologies

MLOps ToolingExperiment TrackingModel VersioningFeature StoresInference Serving Frameworks

Industry Keywords

Financial MarketsAIMLNLPApplied Engineering

Tech Stack

Tools & technologies

Rust

About the role

Key responsibilities & impact

Refactor research-quality and prototype ML code into production-ready, maintainable, deployable systems
Own end-to-end deployment of ML pipelines, including data ingestion, feature engineering, model serving, monitoring, and reliability
Integrate structured development practices such as task management, Git workflows, code reviews, and CI/CD
Collaborate with researchers and the broader team to translate intent into observable production systems
Identify and address architectural and engineering quality issues
Build and maintain monitoring, logging, and alerting infrastructure
Support the transition toward Rust-native infrastructure
Participate approximately 25% in R&D while focusing approximately 75% on deployment and applied engineering

Requirements

What you’ll need

Experience in a professional, structured, team-based engineering environment
Experience with architecture decisions, rigorous code reviews, task management, Git workflows, CI/CD, and shared ownership of systems
Builder-oriented approach to turning research and prototype work into production-grade systems
Collaborative experience working alongside strong engineers and sharing system ownership
Curiosity about AI and ML systems
Ability to work with high autonomy in a fast-moving, ambiguous environment
Curiosity about financial markets
Rust proficiency or strong willingness to ramp quickly
Exposure to ML/AI concepts such as model training, inference pipelines, or working alongside ML researchers
Experience with NLP or text-based signal extraction as an applied engineering discipline
Experience in or curiosity about financial markets
Familiarity with MLOps tooling, including experiment tracking, model versioning, feature stores, or inference serving frameworks
Overlap with U.S. business hours is expected

Benefits

Comp & perks

Remote-first, distributed team with frequent in-person sprints and retreats
Competitive total comp with strong bonus upside tied to performance
Hardware & home-office stipend
Conference and learning budgets
Comprehensive health benefits (medical/dental/vision)
Life insurance
Open vacation policy
Flexible work hours and location
16 weeks fully-paid parental leave + supported return to work
Retirement matching