Senior ML Engineer: Real-Time Ad Infrastructure (Remote)

Hidden Jobs; United States; Remote; USD 122,000 - 168,000; Full time

Benefits offered by this job

Home office reimbursements

Learning budget

Health benefits from day one

Job summary

StackAdapt is seeking a senior-level engineer to advance our data science platform. You will design and optimize real-time data pipelines, build scalable ML-serving microservices, and translate research questions into concrete engineering plans.

Collaborate with data scientists, backend engineers, and leadership to push performance at very high scale. You will own critical components of the ML infrastructure, drive architectural improvements, and implement low-latency algorithms across thousands

Qualifications

• Break down loosely defined problems into structured work.
• Proven track record delivering complex tech projects independently and through coordination.
• Strong CS fundamentals: algorithms, data structures, concurrent systems.
• Hands-on experience implementing probabilistic or machine learning algorithms.
• Experience designing scalable distributed systems.

Responsibilities

• Design modular, scalable real-time data pipelines capable of handling massive datasets.
• Propose and coordinate architectural improvements to large-scale ML data pipelines.
• Implement custom machine learning algorithms optimized for low-latency environments.
• Build and maintain microservice architectures that train, serve, and monitor thousands of ML models concurrently.
• Translate ambiguous research and product questions into concrete, actionable engineering plans.

Skills

Problem decomposition

Project coordination

Algorithms

Concurrency

Distributed systems

ML algorithms

Education

Computer science degree

Job description

StackAdapt is seeking a senior-level engineer to advance our data science platform. You will design and optimize real-time data pipelines, build scalable ML-serving microservices, and translate research questions into concrete engineering plans.

Collaborate with data scientists, backend engineers, and leadership to push performance at very high scale. You will own critical components of the ML infrastructure, drive architectural improvements, and implement low-latency algorithms across thousands