Data Platform Engineer at Jobgether – United States

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Data Platform Engineer in United States.

This role sits at the core of building and scaling the data and machine learning infrastructure that powers next-generation AI-driven products and analytics experiences. You will design and evolve foundational platforms that enable self-serve analytics, model deployment, and real-time data access across the organization. Working at the intersection of data engineering, infrastructure, and machine learning, you will help create systems that make data and AI capabilities accessible, reliable, and production-ready. The environment is highly collaborative, innovation-driven, and focused on building scalable, modern platforms that directly influence product experience and business impact. You will partner closely with Data Science, Product, and Infrastructure teams to unlock new AI capabilities and improve developer productivity. This is a high-impact opportunity for an engineer passionate about building robust, forward-looking data systems at scale.

• Build and evolve the end-to-end data and ML platform, including model serving, feature pipelines, workflow orchestration, CI/CD for ML systems, and production monitoring.
• Lead development of AI-powered data agent systems enabling self-serve analytics, including prompt-processing pipelines, instrumentation, and usage analytics.
• Design and implement scalable data products and product-facing data systems that integrate models and data directly into user experiences.
• Develop platform tooling that enables Data Science and ML teams to efficiently deploy, test, and operate models in production environments.
• Architect infrastructure for natural language and AI-assisted data interfaces, enabling advanced self-serve analytics capabilities.
• Drive cross-functional alignment on data contracts, SLAs, and system design across engineering, data science, and product organizations.
• Improve developer experience by building robust abstractions, internal tools, and scalable platform capabilities for ML and data practitioners.

Requirements:

• 5+ years of experience in data platform engineering, infrastructure, or machine learning engineering, including hands-on work with AI/ML systems.
• Experience building and operating end-to-end ML systems in production, including training, deployment, evaluation, and monitoring.
• Strong programming skills in Python or similar languages, with experience building scalable and reliable distributed systems.
• Proven experience designing ML infrastructure such as feature stores, model serving systems, or workflow orchestration platforms.
• Strong cross-functional collaboration skills with experience working across Data Science, Engineering, Product, and Infrastructure teams.
• Solid understanding of data modeling, data pipelines, and data product design principles.
• Familiarity with modern ML and data tooling is a plus (e.g., MLflow, Kubeflow, CI/CD pipelines, feature stores).
• Experience with LLMs, RAG systems, prompt processing, or AI-native infrastructure is highly valued.
• Exposure to modern data stack technologies such as Snowflake, dbt, Dagster, or cloud platforms like AWS is a plus.
• Strong product mindset with the ability to connect platform work to user and business impact.

Benefits:

• Annual base salary range: $235,000 – $376,000 USD, depending on location, experience, and qualifications.
• Equity packages as part of total compensation.
• Performance-based bonuses and eligibility for annual incentive plans (depending on role).
• Comprehensive health coverage including medical, dental, and vision insurance.
• Retirement benefits with company contributions (401(k) plan).
• Generous paid time off, company recharge days, and paid holidays.
• Work-from-home support, learning and development stipend, and cell phone reimbursement.
• Mental health, wellness, fertility, and family planning support programs.
• Flexible work arrangements depending on role and team needs.
• Inclusive, innovation-focused culture emphasizing learning, growth, and collaboration.