ML Architect

ML Architect

Location: Remote, Nationwide

Ensure you read the information regarding this opportunity thoroughly before making an application.

Our client is building advanced AI systems designed to move sophisticated machine learning capabilities from experimentation into dependable, real-world production. They are seeking an ML Architect to take hands on technical ownership of large-scale model architecture, distributed computing, GPU performance, and the systems required to operate modern AI reliably at scale. This is an opportunity for a deeply technical ML leader to solve difficult zero to one challenges, influence engineering standards, and help shape how next generation intelligent products are built.

This Role Offers

• High impact architectural ownership across the complete machine learning lifecycle, from data and training through production inference.
• A highly technical environment where individual contribution, sound judgment, and rapid experimentation are valued.
• Competitive cash and equity compensation with health, dental, life insurance, and 401(k) benefits.

Focus

• Define and evolve production ML architecture spanning data preparation, model development, evaluation, inference, deployment, and operational feedback.
• Engineer scalable distributed training and inference environments across GPU infrastructure using modern parallelization and systems approaches.
• Lead GPU and serving optimization efforts focused on memory utilization, quantization, mixed precision, throughput, latency, cost, and production capacity.
• Build dependable ML platforms and services with strong standards for maintainability, observability, reliability, and production readiness.
• Own ambiguous, zero to one ML initiatives and make pragmatic architectural tradeoffs involving model quality, performance, cost, reliability, and safety.
• Partner with research and application engineering teams to translate emerging model capabilities into stable, scalable production experiences.
• Develop robust data and evaluation workflows that support model quality measurement, responsible deployment, experimentation, and continuous improvement.
• Set technical direction across complex ML systems, resolve difficult architecture and performance challenges, and establish scalable engineering practices for other technical contributors.

Skill Set

• Advanced knowledge of AI model development, including practical experience building, optimizing, and implementing complex neural network solutions in real-world environments.
• Strong background leveraging scalable machine learning platforms and parallel computing tools to support complex model development and high-volume AI workloads.
• Demonstrated GPU performance expertise across memory efficiency, quantization, mixed precision, latency reduction, throughput improvement, and production scaling.
• Strong software engineering fundamentals with a track record of building robust, maintainable, production grade machine learning systems.
• Proven ability to own architecture end to end across data, training, evaluation, inference, deployment, and ongoing production operations.
• Principal level technical influence, including establishing engineering standards, guiding technical contributors, and solving complex architectural and performance problems.
• Strong command of PyTorch, JAX, or another modern ML framework, with the ability to work effectively across evolving machine learning technology stacks.
• Experience with technologies such as high-performance LLM serving frameworks, GPU kernels or compilers, reinforcement learning workflows, multimodal models, or large scale data processing is beneficial.

About Blue Signal:

Blue Signal is an award-winning, executive search firm specializing in various specialties. xhyhwjd Our recruiters have a proven track record of placing top-tier talent across industry verticals, with deep expertise in numerous professional services. Learn more at /46Gs4yS