STAFF ML ENGINEER (SF)

Agencygrid - San Francisco, CA

Full-time - Mid Level

San Francisco, CA

Heavy and Civil Engineering Construction

About the position

Grid is on a mission to level the financial playing field by building innovative financial products that empower users to manage their financial futures effectively. As a Staff Machine Learning (ML) Engineer, you will play a crucial role in scaling our core product lines and enhancing our data-driven approach. You will collaborate closely with data scientists, product managers, and engineering leaders to implement and scale statistical inference systems that are vital to our operations. Your work will have high visibility and significant impact on Grid's business performance, as you will be involved in projects that include fraud detection, risk underwriting, and predictive analytics. In this role, you will be responsible for architecting and developing machine learning infrastructure that supports both offline research and online serving. You will have the opportunity to set the standard for Grid's statistical inference and machine learning practices, ensuring that our models are accurate and precise at every layer, from development to deployment. The position offers a unique chance to work in a fast-paced environment where your contributions will directly influence the success of our financial products. The tech stack at Grid is built on GCP, Go, protobufs, BigQuery, and MySQL, optimized for clean data sources and robust data warehousing. As the first full-time ML Engineer, you will have the autonomy to identify key leverage points for machine learning implementations and help foster a strong data science culture within the company. You will be expected to implement and deploy models that align with strategic business objectives, enabling growth, mitigating fraud, and controlling risk in a complex financial landscape.

Responsibilities

• Research and select modern implementation strategies for machine learning to ensure a smooth transition from research to production.
• Implement and deploy models that enable strategically relevant business objectives, such as enabling growth, mitigating fraud, and controlling risk.
• Enable rapid iteration in a complex, financially sensitive environment that demands a high degree of accuracy, precision, and control.
• Set up and operate ML research infrastructure as needed by data scientists across the company, including data lake management and ML ops.
• Ship production-worthy software with all necessary details required for modern devops, including testing, observability, and automation.
• Collaborate with data scientists, product engineers, business managers, and more to ensure seamless integration of data products and APIs with the overall tech stack.
• Help build out data science as a team and practice at Grid.

Requirements

• Proven experience in data science and machine learning, including a strong background in statistical inference and machine learning frameworks and techniques (e.g., Logistic Regression, Naive Bayes, Random Forest).
• Full in-depth comprehension of relevant tools, frameworks, and architectures to ensure solutions are correct, holistic, and scalable.
• Ability to work independently and take ownership of projects, showcasing a proactive approach to identifying key leverage points for data products.
• Demonstrated experience or understanding of the financial industry, especially in the context of building and scaling FinTech products.

Nice-to-haves

• Curiosity and optimism, with a constant desire to understand and improve the world around you.
• Confidence to prioritize work and deliver demonstrable results on a tight cadence.