Founding Machine Learning Engineer

Plutus; Kirkland (WA); On-site; USD 180,000 - 240,000; Full time

Job summary

Plutus is seeking a founding machine learning engineer to scale from v1 to a billion under management by year-end. You will design and deploy data pipelines and models that surface insight to customers, not just internal dashboards.

You will own end-to-end experiments, collaborate with product and engineering, and operate in ambiguity at an early-stage startup. You should have strong ML fundamentals, experience taking models to production, and a passion for turning data into product, with a

Qualifications

• Strong fundamentals in machine learning and applied data science.
• Hands-on experience building and operating data pipelines at scale.
• Strong software engineering fundamentals and maintainable code.
• Extreme ownership in an early-stage startup environment.
• Deep curiosity about the customer and product-driven mindset.
• Genuine belief in our mission and financial motivation as a feature.

Responsibilities

• Turn data into product by designing and building data pipelines and models.
• Focus on outcomes and product goals, not just modeling modality.
• Help drive retention by understanding customer needs and timing.
• Work as an engineer first with strong product sense and shipability.
• Live in the customer's shoes and close gaps between product promises and reality.
• Behave like an owner in a small, fast-moving team.

Skills

ML fundamentals

Data pipelines

Software engineering

Startup ownership

Customer focus

Mission alignment

Job description

Plutus is a fast growing company on a mission to democratize access to investing. We're an SEC-registered investment advisor giving everyday investors the kind of portfolio strategy and automated execution that used to be reserved for institutions and the ultra-wealthy. We launched in January, AUM is already in the tens of millions, and we're now looking for a founding machine learning engineer to help take us from v1 to a billion under management by end of year.

We have an enormous amount of customer and portfolio data and not nearly enough of it turned into insight yet. That gap is the job.

Responsibilities

• Not cool, useful - We won’t ask you to prove you used a CNN or transformer model for a job where basic linear regression is sufficient. You will focus on outcomes and product goals without care for the modality!
• Turn data into product - design and build the data pipelines and models that surface insight back to customers, not just dashboards for internal use.
• Help drive retention above 98% - the better we understand what a customer needs and when, the better we keep them. That's on you as much as it's on product.
• Be an engineer first, with product sense - you'll work closely with product and eng, and you should know what's buildable and what isn't, so the things you propose are grounded in what's actually shippable.
• Live in the customer's shoes - regardless of your experience level, the job is finding the gap between what we say the product does and what it actually does, then closing it with data.
• Behave like an owner - the team is small, the stakes are real, and people are here on weekends because they want to see the result of their work, not because anyone's asking them to.

Required

• Strong fundamentals in machine learning and applied data science, with real experience taking models from prototype to production
• Hands‑on experience building and operating data pipelines at scale
• Strong software engineering fundamentals - you can write clean, maintainable code, not just notebooks
• Extreme ownership and genuine comfort operating in ambiguity at an early‑stage startup
• Deep curiosity about the customer - you notice when something doesn't quite work, and you can't help but go fix it
• Financial motivation is a feature, not a bug, here - but you need to actually believe in the mission, not just be looking for a paycheck