ML Engineer

Blue Signal Search; United States; On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Life insurance

401(k) match

Job summary

Blue Signal Search is seeking an ML Engineer to bridge machine learning and software engineering, building a production foundation for deploying, evaluating, monitoring, and improving models. You will own the ML lifecycle, solve infrastructure challenges, and help shape how advanced AI capabilities reach production.

The role emphasizes dependable production foundations, scalable model serving, automated workflows, and visibility into model health, with equity and comprehensive benefits in a

Qualifications

• Strong software engineering fundamentals for production-grade systems.
• Hands-on experience with ML infrastructure and production ML workflows.
• Experience with model serving, evaluation, or data pipelines.
• Knowledge of distributed computing, fault tolerance, and scalable design.
• Ability to write clean, maintainable code for long-term production use.
• Familiarity with Python and ML frameworks like PyTorch or JAX.

Responsibilities

• Engineer dependable production foundations for ML workloads from development to deployment.
• Create scalable model execution services with latency and throughput goals.
• Develop automated workflows for data prep, validation, releases, and iteration.
• Establish evaluation frameworks to monitor model behavior and regressions.
• Implement monitoring to visualize health and performance of production ML workloads.
• Identify bottlenecks and improve performance across the pipeline.

Skills

Production-grade systems

Python

Distributed computing

Troubleshooting

Code quality

Ownership

Tools

PyTorch

JAX

Job description

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