ML Engineer

ML Engineer

Location: Remote, Nationwide

Our client is building next-generation AI systems designed to move beyond experimentation and deliver dependable, real-world experiences at scale. They are seeking an ML Engineer who can bridge machine learning and software engineering, creating the production foundation that allows models to be deployed, evaluated, monitored, and improved with confidence. This is an opportunity to take meaningful ownership of a modern ML environment, solve challenging infrastructure problems, and help shape how advanced AI capabilities reach production.

This Role Offers:

• High-impact ownership across the production machine learning lifecycle, with the opportunity to influence architecture, engineering practices, and technical direction.
• A culture that values experimentation, independent problem solving, continuous learning, and engineers who take ideas from concept through production.
• A compensation package individually structured with cash and equity, along with health, dental, life insurance options, and a 401(k) match.

Focus:

• Engineer dependable production foundations that support machine learning workloads from development through deployment and ongoing operation.
• Create scalable model execution services designed to perform consistently under demanding latency and throughput requirements.
• Develop automated workflows that make data preparation, model validation, releases, and iterative improvement easier to operate and reproduce.
• Establish practical evaluation frameworks that help engineering teams understand model behavior, performance changes, and quality regressions.
• Implement monitoring and diagnostic capabilities that provide clear visibility into the health and behavior of production ML workloads.
• Investigate performance constraints throughout the machine learning lifecycle and deliver improvements across speed, capacity, resilience, and infrastructure efficiency.
• Partner with machine learning, research, product, and software engineers to translate evolving technical needs into robust production solutions.
• Turn recurring infrastructure needs into reusable engineering capabilities that accelerate future development and reduce duplicated effort.
• Contribute to an adaptable ML environment capable of incorporating new model architectures, serving approaches, and infrastructure techniques as the technology evolves.

Skill Set:

• Strong software engineering fundamentals with demonstrated experience developing and supporting production-grade systems.
• Hands-on experience engineering ML infrastructure, shared ML capabilities, or machine learning applications operating in production.
• Practical experience with one or more areas such as model serving, inference, evaluation systems, deployment workflows, or ML data pipelines.
• Strong knowledge of distributed computing concepts, fault tolerance, operational reliability, and scalable system design.
• Ability to produce clean, maintainable, well-structured code suitable for long-term production use.
• Experience with Python and exposure to modern machine learning frameworks such as PyTorch or JAX is highly relevant.
• Familiarity with cloud environments, workflow orchestration, GPU-based computing, or high-performance model serving is valuable.
• Exposure to technologies supporting LLM inference, retrieval systems, or vector-based data architectures is a plus.
• Strong analytical and troubleshooting skills with an interest in identifying system bottlenecks and improving performance.
• Comfortable navigating ambiguity and rapid technical change while taking ownership of problems from investigation through implementation.

About Blue Signal:

Blue Signal is an award-winning, executive search firm specializing in various specialties. 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 bit.ly/46Gs4yS