Applied ML Engineer – Model R&D
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Job Description
Applied ML Engineer turning AI/ML research into scalable production systems at Nascent, an open-markets investment firm. Deploying pipelines, monitoring infrastructure, and Rust-native systems.
Responsibilities:
- 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:
- 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:
- 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
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