Machine Learning Engineer

About FINNYFINNY is a growth platform for financial advisors. We are on a mission to make great financial advice easier to find. Today, access to quality financial guidance is limited, not because advisors don’t exist, but because the right connections are hard to make when it matters. We’re fixing that with AI-powered tools that help advisors find, engage, and retain the clients they can genuinely help.

We’ve raised a 4.3 million pre-seed from Y-Combinator in S24 and now $17M Series A led by Venrock. We work with firms across the wealth management ecosystem, and have been recognized as a leader in fintech innovation—winning #1 at the Morningstar Fintech Showcase and being featured across the industry.

We’re based in Chelsea, NYC, building fast and ambitious systems at the intersection of data, AI, and real-world wealth services.

About The Team

Being a Machine Learning Engineer at FINNY means owning the models that power search, matching, ranking, recommendations, data and intelligent automation across the product. This is a model first role. While you’ll work with real production data and pipelines, your primary impact comes from designing, training, evaluating, and improving ML systems that directly shape user outcomes.

You’ll partner closely with product, backend, and frontend teams to turn ambiguous problems into measurable model improvements.

What You’ll DoHelp build FINNY’s core modelsDesign, train, and iterate on custom models that power data imputation, prospect and audience recommendations, campaign customization and personalization, and automations.

• Build & improve models in production
• Take models from research → experimentation → deployment → iteration
• Own offline evaluation, online metrics, and feedback loops
• Improve model performance over time through better objectives, features, and training strategies—not just more data
• Advanced modeling & experimentation
• Apply and adapt techniques such as:
• Operate effectively in low-signal, noisy, or cold-start environments
• Contribute to ML systems & infrastructure
• Work with backend engineers to productionize models reliably and at scale
• Help define standards for model versioning, evaluation, deployment, and monitoring
• Influence long-term ML strategy and reduce technical debt in modeling workflows
• What We’re Looking For
• You’re a model builder at heart
• You care deeply about how models learn, not just how pipelines run
• You’re comfortable reasoning about loss functions, tradeoffs, and evaluation
• You enjoy designing solutions when the problem is underspecified and data is imperfect
• You’re strong technically
• Very strong Python with extensive hands-on experience building ML systems.

Strong statistical and mathematical foundations.

• Proven experience training, fine-tuning, and deploying custom models into production, not just experimentation or offline research
• Experience designing loss functions, evaluation metrics, and validation strategies aligned with real-world product objectives
• Familiarity with model lifecycle management: versioning, reproducibility, monitoring, and iteration in production environments
• You’ve shipped ML systems before
• You’ve taken models beyond notebooks and into real products
• You understand failure modes, monitoring, and iteration in production MLStartup experience
• Your working style
• You tackle ambiguity head-on and turn fuzzy problems into concrete experiments
• You move fast, iterate, and aren’t precious about first approaches
• You communicate clearly about model behavior, limitations, and tradeoffs
• In-person, NYC (5 days/week in Chelsea office)
• Compensation &

Benefits

FINNY offers a competitive compensation package including:

Competitive salary and equityMedical, dental, and vision insuranceFlexible paid time off401(k)Food and meals provided in our NYC officeTeam offsites and events

Equal Opportunity Employer

FINNY is an equal opportunity employer. We are committed to diversity and inclusion in our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.