Machine Learning Engineer

A fast-growing FinTech company is hiring a Senior ML Engineer to help take machine learning from "works in a notebook" to "runs in production, at scale" - powering the fraud, risk, and pricing decisions that actually drive the business. This isn't a research role, and it's not a narrow specialism either: it's a hands-on senior seat where the model, the pipeline, and the production system are all your problem to own.Company CultureOutcome-obsessed - the team cares about the model working in production, not just performing well in a notebookFlat structure, high ownership - minimal layers between building something and shipping itRegulated but not slow-moving - used to working with real-time, high-volume data without treating compliance as a blockerBackground-agnostic hiring - the best person for this might come from ML, product engineering, or infrastructureSmall, senior team - you'll work directly with founders and leadership, not through layers of managementLow-ego, high-curiosity - people say what they think, disagree openly, and move on fast once a decision's madeWhat's on OfferSalary: $180,000 - $230,000EquityFull medical, dental, and vision coverageFlexible PTO401(k) match$2,000 annual learning and development budgetHome office/equipment stipend for remote setupLocation: Remote (US), with SF as HQ for those who want to be in-officeWhat You'll Be DoingOwning the end-to-end lifecycle of ML systems - from prototype to reliable, monitored productionSetting technical direction on how the team builds and scales ML infrastructure and pipelinesWorking across real-time or high-volume data in a regulated environmentMonitoring, debugging, and improving model and system performance over timeMentoring more junior engineers and raising the technical bar across the teamPartnering closely with leadership on how ML capability translates into product and business valueWhat We're Looking ForCore Requirement5+ years of hands-on experience shipping ML systems, or ML-adjacent infrastructure, into productionComfortable owning both the model and the plumbing around it, and setting the technical approach for how that gets doneAlternative Backgrounds WelcomeSenior product engineers who've worked closely with ML systems and shipped production features around themSenior infrastructure/platform engineers who've built and owned the data pipelines or deployment systems ML depends onEngineeringStrong Python and SQLComfort with Spark/Kafka for data, PyTorch/TensorFlow if touching models directlyExperience with AWS or GCP, Docker/Kubernetes, and orchestration tools like AirflowTrack record of making architectural decisions, not just implementing someone else'sIf this is interesting to you, please do apply!