Applied ML EngineerTheOpportunityYou'll own the last mile - taking promising AI/ML concepts from research and making them reliable, scalable, and production-functional. As an Applied ML Engineer at Nascent, you'll sit at the intersection of a strong R&D team and the systems that consume their work, bringing structured engineering discipline to close the gap between prototype and production. The split is roughly 75% deployment and applied engineering, 25% participating in R&D. Your job is to make things work, stay working, and scale.
The team is full of strong researchers and ML minds. We are targeting experience in a professional engineering environment where architecture decisions matter, task management is a given, and shared ownership of systems is how you build. You'll bring that discipline and push the team's engineering standards forward. You'll be the person who says "here's how we'd build this properly" and then builds it.
You'll thrive here if you're a builder who gets energy from taking messy, promising work and turning it into something maintainable and production-grade - and if you want to do that in a high-autonomy, performance-driven environment where your output shows up in P&L. This role is remote-first; overlap with U.S. business hours is expected.
Responsibilities- Take research-quality and prototype ML code from the R&D team and refactor it into production-ready, maintainable, deployable systems - closing the gap between "it works" and "it runs reliably at scale."
- Own end-to-end deployment of ML pipelines: data ingestion, feature engineering, model serving, monitoring, and reliability.
- Integrate structured development practices - task management (Jira/Linear), Git workflows, code reviews, CI/CD - on a team that moves fast and needs the discipline to match.
- Collaborate daily with researchers and the rest of the team to translate their intent into working, observable, production systems.
- Identify and call out architectural and engineering quality issues; be the voice that says "this is how we'd do it properly" and then does it.
- Build and maintain monitoring, logging, and alerting infrastructure so the team knows when things break before the trading desk does.
- Support the team's transition toward Rust-native infrastructure as the codebase evolves.
About you- You came up in a real code shop - a structured, team-based engineering environment where architecture decisions were debated, code reviews were rigorous, and task management was just how work got done. Not focused on solo projects. Not academia. A professional dev environment.
- You are a builder first. Research is interesting to you, but shipping is what gets you up in the morning. You take messy, promising work and make it production-grade - and you take pride in that.
- You are collaborative by nature. You've worked alongside strong engineers, shared ownership of systems, and given and received hard feedback on code. You know what good looks like because you've seen it.
- You have strong opinions about how software should be built, and you're not shy about expressing them - but you back it up with work, not just words.
- You are curious about AI and ML systems - not necessarily an expert, but genuinely interested in how they work and eager to learn from a team that lives in this space.
- You don't need all the answers handed to you. High autonomy, fast-moving environment, ambiguous problems - that's where you do your best work.
- Finance doesn't need to be your background, but you're curious about markets.
Preferred experience- Rust proficiency or strong willingness to ramp quickly - the team is moving Rust-native and needs someone who can read and eventually contribute to Rust codebases.
- Exposure to ML/AI concepts - model training, inference pipelines, or working alongside ML researchers - even if not as a primary specialization.
- Experience with NLP or text-based signal extraction as an applied engineering discipline.
- Experience in or curiosity about financial markets
- Familiarity with MLOps tooling: experiment tracking, model versioning, feature stores, or inference serving frameworks.
What We Offer- The opportunity to learn, experiment and build in an entrepreneurial environment
- 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 as well as flexible work hours and location
- 16 weeks fully-paid parental leave + supported return to work.
- Retirement matching, open vacation policy, flexible hours.
