Performance Engineer

Tangier Partners; Boston (MA); On-site; USD 150,000 - 210,000; Full time

Job summary

Tangier Partners is partnering with a well-funded AI research company to hire a Performance Engineer focused on scalable compute efficiency. You will own how compute infrastructure runs at scale, profile GPU workloads, and drive capacity planning from the ground up.

You will shape performance strategies across research and production, collaborating with engineers to translate workload patterns into robust infrastructure requirements and tooling for long-term improvement.

Qualifications

• Hands-on experience optimizing GPU workloads and distributed computing environments.
• Strong grasp of systems performance profiling, benchmarking, and capacity planning.
• Experience with large-scale compute infrastructure in research or AI environments.
• Ability to operate with high autonomy and bring structure to a greenfield function.
• Comfort at the intersection of research and engineering.

Responsibilities

• Profile and optimize GPU and compute-intensive workloads across research and production systems.
• Build capacity models to forecast compute needs as the business scales.
• Identify performance bottlenecks across distributed systems and drive resolution.
• Partner with research and engineering teams to understand workload patterns and translate them into infrastructure requirements.
• Define the frameworks and tooling the team will use to measure and improve system performance over time.

Skills

GPU optimization

Distributed computing

Systems performance profiling

Benchmarking

Capacity planning

Autonomy

Research & engineering collaboration

Job description

We are partnering with a well-funded AI research company on a mission to push the boundaries of scientific discovery at scale, and they need someone who obsesses over performance at every layer of the stack.

As their Performance Engineer, you will own how efficiently their compute infrastructure runs at scale. You will profile GPU workloads, identify bottlenecks across distributed systems, and ensure machines are running at peak performance as research and data volumes grow. This is not a reactive role. You will be shaping how the business thinks about compute, capacity planning, and long-term infrastructure efficiency from the ground up.

What you will be doing

• Profiling and optimizing GPU and compute-intensive workloads across research and production systems
• Building capacity models to forecast compute needs as the business scales
• Identifying performance bottlenecks across distributed systems and driving resolution
• Partnering closely with research and engineering teams to understand workload patterns and translate them into infrastructure requirements
• Defining the frameworks and tooling the team will use to measure and improve system performance over time

What we are looking for

• Hands-on experience optimizing GPU workloads and distributed computing environments
• Strong grasp of systems performance profiling, benchmarking, and capacity planning
• Experience working with large-scale compute infrastructure, ideally in a research or AI-driven environment
• Ability to operate with high autonomy and bring structure to a greenfield function
• Comfort working at the intersection of research and engineering