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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Engineer, Machine Learning - **Company:** Current Engineering Inc - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $265,000.0 - $325,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Distributed Computing Environment, Java Virtual Machine (JVM), Python (Programming Language), Machine Learning, Azure Machine Learning, Software Engineering, SQL Databases, Data Streaming, Apache Spark, Change Data Capture, Machine Learning Operations, Apache Beam - **Published:** September 29, 2026 - **Apply:** https://startup.jobs/staff-software-engineer-machine-learning-current-10216319 ## About the Role * 3+ years experience building and operating ML systems in production, including feature pipelines, the training data path, the serving layer, and the monitoring around them * A track record of improving ML delivery workflows, and the ability to explain the trade-offs, results, and lessons from those decisions * 8+ years of overall software engineering experience, including strong production skills in Python and SQL, experience building production systems in a JVM language, and 3+ years of experience building and maintaining ML platforms * Sound reasoning about time in data: point-in-time correctness, label leakage, feature availability, and training/serving skew * Experience setting a long-term technical direction and turning it into an achievable roadmap, delivering useful improvements along the way * Experience leading initiatives from an ambiguous problem through scoping, stakeholder agreement, and delivery * Experience establishing engineering standards, mentoring engineers, and helping teams adopt shared infrastructure * Strong communication skills, with the ability to explain trade-offs clearly and find workable solutions across engineering, data science, risk, marketing, and finance * Fluency with AI tools, including coding agents, in your own engineering work, with the judgment to evaluate their output and own the quality of what you ship * Feature store, feature platform, or ML platform experience at a company where models make consequential decisions is a plus * Experience in financial services, credit, fraud, or another regulated decisioning domain, and familiarity with model risk management, is a plus * Experience with streaming and change data capture, large-scale batch on Apache Beam or Spark, or distributed training is a plus ## Description We are looking for a Staff Engineer, Machine Learning to join our Infrastructure team in New York. This role has a salary range of $265,000 - $325,000. You will lead ML engineering initiatives across Current, with the goal of optimizing our model lifecycle: improving how we build, validate, deploy, and change models, and making that path faster and more repeatable as our model portfolio grows. This is a hands-on individual contributor role without direct reports, with room to grow into a team. The ideal candidate has built and operated ML systems in production end to end, not only models, and has a track record of setting technical direction and delivering against it. This person should be comfortable leading from an ambiguous problem to a shipped solution, and should treat data scientists as their customer., * Owning technical direction for the ML stack end to end: feature definition and computation, training data generation, training infrastructure, model serving, and production monitoring, along with the contracts between them * Building tooling for training/serving consistency across analytics, batch computation, and live serving, accounting for differences in data sources and timing * Designing how every deployed model stays linked to its dataset, feature versions, labels, and training code, to the standard model risk management expects * Enabling data scientists to generate reproducible, point-in-time-correct datasets and run standard validation without an engineering ticket * Setting the working contracts between the groups that build, consume, and govern models, and keeping the stack legible to people who don't read the code * Measuring delivery time, engineering effort, and rework, and using that evidence to prioritize improvements * In your first year: * Establishing a delivery baseline and proving the workflow on one production model with versioned features, a reproducible dataset, and reusable validation * Extending those capabilities to additional models and measuring adoption and improvement against the baseline * Standardizing model monitoring and defining production-readiness gates with Data Science, Risk, and service owners * Evaluating build-versus-buy options for ML platform tooling against real production requirements Partnering daily with engineers across our squads and with data scientists and analysts, and regularly with Risk, Marketing, and Finance, who own the decisions our models support ## Related Videos - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Fireside Chat: Deep Learning, Deep Impact: Harnessing AI for Language Innovation](https://www.wearedevelopers.com/videos/612-fireside-chat-deep-learning-deep-impact-harnessing-ai-for-language-innovation) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Let's Get Aggregated: Custom UDAFs in Spark ](https://www.wearedevelopers.com/videos/1649-let-s-get-aggregated-custom-udafs-in-spark) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)