Remote Machine Learning Engineer - Inference / Serving

Description:

• Yobi is a rapidly growing Behavioral AI company focused on ethically democratizing the benefits of data and AI.
• The company has built one of the largest consented behavioral datasets in the United States since 2019, extending beyond traditional Big Tech.
• Yobi constructs foundation models of human behavior based on real-world actions like purchases and store visits.
• The company’s private-by-design modeling enables advanced personalization and decision-making for brands while ensuring privacy, safety, and ethics.
• Yobi aims to bring the performance of closed-web user acquisition to the open web and connected TV.
• The role involves designing, optimizing, and operating systems that implement Behavioral AI models in real time.
• Engineers at Yobi own significant areas of the architecture and culture, building systems from the ground up and defining the ethical scaling of Behavioral AI.
• The company is well-funded with over five years of runway and is rapidly scaling revenue, projecting to break even by 2026.
• Yobi has partnerships with Microsoft and Databricks and offers fully remote or hybrid work options from several hubs including SF Bay Area, Seattle, and NYC.
• The team consists of Machine Learning experts with experience at leading tech companies like Amazon, Uber, Twitter, and Meta.

Requirements:

• Candidates must have deep expertise in model deployment, having built or scaled production ML serving systems that handle versioning, rollouts, rollback strategies, and live experimentation.
• A low-latency mindset is essential, with an understanding of model graph optimization, quantization, caching, batching, and efficient feature retrieval.
• Candidates should possess systems fluency, writing robust, high-performance code in Go, Rust, C++, or Java, and be comfortable integrating with Python for model analysis.
• Operational maturity is required, treating inference as a living system by monitoring drift, tracking model lineage, and ensuring observability from input to outcome.
• Infrastructure intuition is necessary to create reproducible and portable serving systems without over-engineering, utilizing custom runtime design, model registries, or lightweight orchestration.
• An applied ML understanding is important, allowing candidates to reason about model performance, interpret trade-offs, and collaborate with researchers to enhance model deployability.

Benefits:

• The position offers a competitive base salary.
• Employees receive meaningful equity and financial upside, representing a real percentage of the company.
• There is an annual bonus target based on personal and company performance.
• Health, dental, and vision insurance plans are provided, with most plans requiring little to no out-of-pocket expenses.
• Employees enjoy unlimited PTO, emphasizing impact over tracking days off.
• A 401k plan with company match is available to employees.