Machine Learning Engineer - Inference / Serving

Yobi AI; New York (NY); Remote; USD 120,000 - 150,000; Full time

Benefits offered by this job

Competitive base salary

Meaningful equity

Annual bonus

Health, dental, vision plans

Unlimited PTO

401(k) with company match

Job summary

A leading Behavioral AI company is seeking a Machine Learning Engineer focused on inference and serving. In this role, you will design and optimize systems to operationalize AI models. The ideal candidate has deep expertise in model deployment, a strong low-latency mindset, and is proficient in languages like Go, Rust, or Java. You will work in either a fully remote or hybrid environment. Competitive salary and equity options are offered.

Qualifications

• Deep expertise in model deployment and production ML serving.
• Strong low-latency mindset and knowledge of inference optimization techniques.
• Operational maturity with monitoring, drift detection, and observability.

Responsibilities

• Build and scale production ML serving systems.
• Ensure low-latency inference by optimizing model graphs.
• Write robust, high-performance code.

Skills

Model deployment and production ML serving

Inference optimization techniques

High-performance code writing

Monitoring and drift detection

Understanding of custom runtimes

Applied ML reasoning

Tools

Go

Rust

C++

Java

Python

Job description

Machine Learning Engineer - Inference / Serving

Join to apply for the Machine Learning Engineer - Inference / Serving role at Yobi AI

Overview

Yobi is a rapidly growing Behavioral AI company on a mission to ethically democratize the benefits of data and AI. Since 2019, we have built one of the largest consented behavioral datasets in the United States, extending far beyond the walled gardens of Big Tech. Unlike traditional LLM companies, Yobi builds foundation models of human behavior grounded in real‑world actions such as purchases and store visits. Our private‑by‑design modeling enables state‑of‑the‑art personalization and decisioning for leading brands and agencies while protecting privacy, safety, and ethics.

Today, we are focused on bringing the performance of closed‑web user acquisition to the open web and connected TV, giving brands walled‑garden results without the walls. At our core, Yobi is building the behavioral intelligence layer for any system that makes a personalization decision.

Working at Yobi

We’re at an inflection point—customer adoption is accelerating, but there’s still room to shape the architecture and culture from the ground up. Engineers here own major surface areas, build 0→1 systems in large‑scale data and model infrastructure, and help define how Behavioral AI scales ethically and effectively.

Highlights

• Well‑funded with 5+ years of runway. We are scaling revenue quickly and project to be breakeven in 2026.
• Partnerships with Microsoft and Databricks.
• Fully remote or hybrid from hubs in SF Bay Area, Seattle, NYC.
• World‑class team of Machine Learning experts with experience at Amazon, Uber, Twitter, Meta, etc.
• Product and Go‑to‑Market teams that have taken ideas from concept to nine‑figure revenue streams.

Benefits

• Competitive base salary.
• Meaningful equity and financial upside.
• Annual bonus target based on personal and company performance.
• Health, dental, vision plans with low out‑of‑pocket costs.
• Unlimited PTO.
• 401(k) with company match.

About the Role

As a Machine Learning Engineer focused on inference and serving at Yobi, you’ll design, optimize, and operate the systems that bring our Behavioral AI models to life in real time. You’ll work at the core of our production environment, turning trained models into performant, reliable, and continuously improving services that power our open‑web and CTV products.

This is an applied ML systems role—equal parts engineering depth, deployment craft, and model intuition. You’ll shape how models are packaged, versioned, rolled out, and observed across environments, ensuring every prediction is fast, accurate, and accountable.

Responsibilities & Expectations

• Build and scale production ML serving systems—handle versioning, rollouts, rollback strategies, and live experimentation.
• Ensure low‑latency inference by optimizing model graphs, quantizing, batching, caching, and efficient feature retrieval.
• Write robust, high‑performance code in Go, Rust, C++, or Java and bridge to Python for model integration and analysis.
• Treat inference as a living system—monitor drift, track model lineage, and ensure observability from input to outcome.
• Make serving systems reproducible and portable without over‑engineering—for instance, custom runtime design, model registries, or lightweight orchestration.
• Reason about model performance and trade‑offs, and work with researchers to deploy more practical models.

Qualifications

• Deep expertise in model deployment and production ML serving.
• Strong low‑latency mindset and knowledge of inference optimization techniques.
• Systems fluency: comfortable writing high‑performance code and bridging to Python.
• Operational maturity: experienced with monitoring, drift detection, and observability.
• Infrastructure intuition: understanding of custom runtimes, registries, and orchestration.
• Applied ML understanding: can interpret performance, reasoning about trade‑offs, and collaborate with researchers.

Seniority Level

Mid‑Senior level.

Employment Type

Full‑time.

Job Function

Engineering and Information Technology. Software Development industry.