Applied ML Engineer - Model R&D
At a glance
Mid-level Machine Learning Engineering role at Nascent. Remote, United States · full-time · no salary stated · we estimate $142,500–$229,847 across the United States.
Nascent has not published a salary for this role. The figure below is our estimate, not theirs.
remoteroles summary, based on the employer's posting.
What you'll do
- Turn research and prototype ML code into maintainable systems that can be deployed and run reliably at scale
- Build ML pipelines from data ingestion and feature work through model serving, monitoring, and reliability
- Bring task tracking, Git practices, reviews, and CI/CD into the team’s development workflow
- Work with researchers to make their ideas observable, working production systems, and flag architectural issues
- Build monitoring, logs, and alerts, while helping the codebase move toward Rust-based infrastructure
What you bring
- You have worked in a professional, team-based engineering environment with architecture decisions, code reviews, and task management
- You build and ship maintainable software, share system ownership, and collaborate through direct code feedback
- You have a genuine interest in AI and ML systems and can learn in a high-autonomy, ambiguous environment
Who this fits
This remote-first role suits an engineer who prefers production delivery to research as a primary focus; the work is described as roughly three-quarters deployment and applied engineering. You’ll need overlap with U.S. business hours. Rust experience or willingness to learn quickly, plus ML exposure, NLP experience, market interest, and MLOps familiarity are preferred rather than stated as core requirements.
From the employer
Applied ML Engineer
The Opportunity
You'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.
About Nascent
Founded in 2020, Nascent exists to build, expand, and capture opportunity, in open markets and permissionless technologies. Building from a base of permanent capital, we deploy assets across a range of both liquid and long-term strategies that ensure we are among the most active users of the open financial system we are helping to build. We’ve backed 100+ early-stage teams that we believe can change markets and expand what’s possible. At Nascent, we combine that venture + market pedigree with product-grade engineering to turn research into revenue: experiments that ship fast, are evaluated rigorously, and move P&L. We focus on models, low-latency infrastructure, and resilient systems that compound performance over time.
Our Team & Culture
We’re an interdisciplinary team of engineers, quants, and operators who treat skill-building like a sport. The culture is competition + curiosity: push to win, share learnings, and iterate publicly through red-teams, internal games, and post-mortems. Autonomy isn’t a perk — it’s how we ship.
Principles that drive our team & work
Compete to win
Explore, experiment, play
Always be building
Seek and speak truth
Own your shit.
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.
We are an equal opportunity employer and welcome diverse perspectives.
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