Software Engineer - ML Infrastructure

About Gray Swan Gray Swan is on a mission to empower the world to use AI safely and securely. We evaluate AI models for the leading frontier labs along with building real-time threat detection and adaptive adversarial red teaming agents for teams deploying AI. We're a team of approximately 70 people, well-funded, growing quickly. Our work directly influences how the world deploys AI agents and systems at scale. Learn more about how we work.

The Role We’re looking for a Software Engineer, AI Infrastructure to work at the intersection of machine learning, software engineering, and production infrastructure. You’ll partner closely with our ML team to build the systems, tools, and workflows that help move models from experimentation into reliable production environments. You’ll improve the developer experience around model training and evaluation, build scalable infrastructure to support ML workflows, and help translate customer requirements and policy considerations into practical technical solutions. This role is ideal for a strong software engineer who enjoys solving complex infrastructure challenges while working closely with ML researchers and engineers to accelerate development and deliver real-world impact.

What You’ll Do:

• Build and maintain the infrastructure, services, and deployment workflows that enable ML models to move from experimentation into reliable production environments.

• Develop internal tools and automation that improve the developer experience for model training, experimentation, evaluation, and iteration.

• Design and optimize systems supporting model evaluation pipelines, experiment orchestration, reproducibility, and performance monitoring.

• Partner with ML engineers and researchers to identify infrastructure bottlenecks and build solutions that accelerate model development and deployment.

• Support customer-specific technical requirements and policy-driven workflows, translating evolving needs into practical, scalable engineering solutions.

• Improve the reliability, scalability, and observability of the systems powering our ML capabilities.

Who You Are:

• A strong software engineer with experience building and maintaining production-grade systems, services, or infrastructure.

• Experienced with Python and comfortable working across backend systems, APIs, and infrastructure code.

• Familiar with cloud infrastructure, distributed systems, containerization, or platform engineering.

• Knowledgeable about ML workflows, including model training, inference, evaluation, or deployment.

• Comfortable collaborating with ML engineers and researchers to translate complex technical requirements into reliable software.

• An owner who can navigate ambiguity, identify problems independently, and deliver practical solutions in a fast-moving environment.

Bonus Points If You Have:

• Experience building ML infrastructure, ML platforms, or internal developer tooling.

• Familiarity with Kubernetes, Docker, and cloud platforms such as AWS or GCP.

• Experience with ML infrastructure tools such as Ray, Kubeflow, MLflow, or Weights & Biases.

• Experience with model serving, distributed training, inference infrastructure, or large-scale evaluation pipelines.

• Exposure to AI safety, AI security, adversarial testing, or model behavior evaluation.

• Experience translating customer requirements, security constraints, or policy considerations into technical implementations.

If you don’t have 100% of these, you should still seriously consider applying. We care more about what you can do than your credentials.

You’ll Thrive Here If You:

• Enjoy working at the intersection of software engineering, infrastructure, and machine learning rather than staying within a single technical domain.

• Are excited to build foundational systems and tooling that help other engineers move faster and work more effectively.

• Take ownership of problems from initial investigation through implementation, deployment, and iteration.

• Thrive in a fast-moving environment where priorities evolve and there isn't always an established playbook.

• Communicate effectively across disciplines and enjoy partnering with researchers, engineers, and other stakeholders.

• Care about building reliable, scalable systems that contribute to the safe and secure deployment of advanced AI.

What We Offer: We offer a competitive compensation package designed to reward impact and incentivize growth. Our compensation philosophy is informed by our current valuation and recent industry data. Compensation: $130,000 - $185,000 plus performance-based bonus and meaningful equity package.

Benefits:

• 401k with up to 4% matching

• 28 days annual leave (vacation + holidays)

• Health, dental, and vision coverage

• Catered lunches (Pittsburgh office)

• Flexible work arrangements

• Visa sponsorship available for exceptional candidates

Interview Process 🔎 Application review. We read everything; we’ll respond within 10 days. 🗣 Recruiter Screen. We learn about you; you learn about us. 🧑‍💻 Technical interview or Hiring Manager Interview. 💻 Role-related assessment. 🗣 Experience & culture interview. 😇 Reference checks. We’ll reach out to 3–5 references that you provide. 📃 Offer. If it’s mutual, we move fast.

How to Apply Submit your resume, link to your portfolio, and answer the questions on the application.