Staff Machine Learning Engineer

We're looking for a Staff Machine Learning Engineer who will serve as a technical leader across one or two small ML Engineering teams, working on key services within our ML infrastructure. This is a hands-on architecture and design role with a strong coding component.

You will work closely with other senior engineers, product teams, and data scientists to define and evolve our ML solutions and infrastructure, ensuring they are robust, scalable, and production-ready., * Act as the Technical Lead for multiple ML squads (3-4 engineers each), driving cross-team execution and elevating engineering standards across the department.

• Own the design, architecture, and scaling of ML services and pipelines
• Guide and review high-level system design, ensuring consistency with platform-wide architectural standards
• Collaborate closely with the Principal Architect to align on long-term technical direction
• Build and maintain production-grade ML systems, ensuring performance, observability, and reliability of those systems
• Contribute high-impact code to core services, with a strong focus on Python, ML modeling, and infrastructure
• Take ultimate accountability for the health of our ML systems, implementing advanced observability frameworks to ensure peak performance and reliability
• Cultivate the next generation of engineers by acting as a role model and mentor, * Learning Budget: Take advantage of our employee development and education budget to enhance your skills.
• Local Language Classes: Participate in language lessons to support your integration and personal development.
• Health insurance package
• Personal and professional growth opportunities
• Regular company events

To ensure a smooth and efficient process and that no applications are overlooked, please apply directly to the role. While we'd love to respond to everyone who reaches out, applying ensures that your application is properly tracked and considered.

Do you have experience in Spark?, * 6+ years of professional experience in ML Engineering, with a demonstrable history of shipping and scaling high-stakes models within SaaS or product-centric environments

• A track record of moving beyond experimental notebooks to building "industrial-grade" ML systems that solve core business problems at scale
• Deep expertise in full MLOps cycle: CI/CD for ML, model versioning, deployment, reproducibility, and monitoring
• Expert-level Python programming with a focus on ML ecosystem (PyTorch, NumPy, Pandas, Scikit-learn) and software engineering best practices.
• Ability to architect low-latency, high-throughput ML systems that integrate seamlessly into a wider platform
• Hands-on experience designing, deploying, and scaling ML systems in a cloud environment (e.g. AWS, Azure, GCP)
• Exceptional communication skills with the ability to translate complex ML concepts into actionable insights for cross-functional stakeholders

Nice-to-haves:

• Proficiency in modern orchestration frameworks (e.g., Airflow, Dagster, Prefect) to manage complex, multi-stage simulation and training pipelines.
• Experience with distributed data processing frameworks like Spark, Flink
• Exposure to simulation, forecasting, or optimization systems
• Domain knowledge in CPG, pricing, or revenue management

"What will customers buy?" is the question at the heart of commercial decision-making-and at Buynomics, we answer it better, faster, and more accurately than any other solution. With our proprietary Virtual Shopper technology, we help companies move from gut feeling to data-driven, customer-centric decisions across pricing, products, promotions, and more. We're building the operating system for commercial decisions-a unified platform that empowers global brands to make optimal, scalable choices. As a next-gen SaaS company, our platform simulates purchasing behavior to support high-stakes, high-scale decisions in real-time.