Machine Learning Engineer
Gray Swan · United States
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- Hybrid
- Full-time
- $203,600 / year
- United States
Job highlights
- Develop advanced ML models for AI safety.
- Build scalable AI systems for real-world impact.
- Advance AI security through research and engineering.
- Collaborate with leading AI safety experts.
- Work in a fast-paced, growing startup environment.
About the role
About Gray Swan
Gray Swan protects organizations from emerging AI security threats. We build real-time threat detection, automated validation, and adaptive defenses for AI labs and enterprises. We ’re a team of ~25 people, well-funded, and growing fast.The Role
As a Machine Learning Engineer at Gray Swan AI, you will play a pivotal role in shaping the future of AI safety solutions. Research at Gray Swan AI is tightly tied to real-world impact. AI security is not a solved problem, and this role is a mix of applied research and system building: developing new approaches to adversarial testing, model evaluation, and robust inference that directly inform how secure AI systems are deployed in practice. You will work at the boundary between research and production, translating novel ideas into scalable AI systems that withstand adversarial pressure. Your expertise in state-of-the-art deep learning architectures, distributed systems, and parallel computing will enable you to tackle complex challenges associated with resource-intensive models. You will be responsible for advancing our methodologies for controlling, monitoring, and analyzing these models, ensuring they meet the rigorous demands of production environments. Join Gray Swan AI to work alongside leading minds in AI safety and apply your technical depth to problems that genuinely matter!What You’ll Do
- Lead the design, development, and deployment of advanced machine learning models to enhance system performance and scalability.
- Tackle complex challenges associated with resource-intensive models using distributed systems and parallel computing.
- Advance methodologies for controlling, monitoring, and analyzing machine learning models in production environments.
- Develop new approaches to adversarial testing, model evaluation, and robust inference.
- Translate research ideas into scalable AI systems deployed in real-world, adversarial settings.
- Work closely with cross-functional teams to ensure research outcomes inform production systems.
Who You Are
Education
Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical field is required.Experience
- Experience in building and deploying machine learning models and systems.
- Demonstrated expertise in designing, training, and deploying deep learning models with frameworks like PyTorch.
- Strong programming experience in Python and C++ (preferred).
- Practical experience developing scalable machine learning pipelines and integrating them with cloud infrastructure (e.g., AWS, GCP, Azure).
- Experience conducting ML research, including building research prototype systems, experiment design, empirical analysis of results, and communicating results via publications.
- Good to have: experience with modern ML methods such as LLMs (training, finetuning, and/or analyzing), synthetic data generation pipelines, and AI safety or security work.
Core Technical Skills
- In-depth knowledge of neural network architectures, including sequence models, transformers, and other state-of-the-art approaches.
- Strong algorithmic problem-solving skills and comprehensive knowledge of ML theory and optimization techniques.
- Proficiency in data preprocessing, transformation, and handling large-scale, multi-modal datasets.
Bonus Points If You Have
- Experience with AI safety practices such as model validation, robustness testing, and continuous monitoring for safety and security incidents throughout deployment.
- Experience with AI safety and security assessments and adversarial testing.
You’ll Thrive Here If
You are genuinely excited by the intersection of research and engineering, and want to both develop new AI safety ideas and see them running in real systems. You are motivated by real-world impact and want your work to directly influence how major AI companies deploy models right now (we work with many of the leading AI labs). You are eager to deepen your AI safety expertise by working alongside a team that includes some of the most respected and influential thinkers in the field. You thrive in a fast-paced, dynamic startup environment where ambiguity is expected. You bring strong collaboration and problem-solving skills, with a focus on driving meaningful, lasting impact.Compensation & Benefits
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.- Salary: $117,300-$203,600
- Equity: Competitive 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.
Key skills/competency
- Machine Learning Engineering
- AI Safety
- Deep Learning
- PyTorch
- Python
- C++
- Distributed Systems
- Parallel Computing
- Adversarial Testing
- Model Evaluation
Skills & topics
- Machine Learning Engineer
- AI Safety
- Deep Learning
- PyTorch
- Python
- C++
- Distributed Systems
- Parallel Computing
- Adversarial Testing
- Model Evaluation
- Machine Learning
- AI
- Software Engineering
- Research
- System Building
- Robotics
- Data Science
How to get hired
- Tailor your resume: Highlight your experience with deep learning, PyTorch, Python, C++, and ML systems deployment.
- Showcase research skills: Emphasize ML research experience, experiment design, and publication record.
- Demonstrate practical application: Detail your experience with scalable ML pipelines and cloud infrastructure integration.
- Prepare for technical interviews: Be ready to discuss ML theory, optimization, and advanced neural network architectures.
- Understand AI safety: Articulate your knowledge of adversarial testing and model robustness.
Technical preparation
Master deep learning architectures (transformers, sequence models).,Practice Python and C++ for ML development.,Build scalable ML pipelines on cloud platforms.,Understand ML theory and optimization techniques.
Behavioral questions
Describe a complex ML challenge you solved.,How do you translate research into production?,How do you handle ambiguity in a startup?,Explain your approach to adversarial testing.
Frequently asked questions
- What is the typical career path for a Machine Learning Engineer at Gray Swan AI?
- As a Machine Learning Engineer at Gray Swan AI, you can expect a career path focused on growth and impact. You'll start by contributing to cutting-edge AI safety research and development, translating novel ideas into production systems. Opportunities for increased leadership in research projects, system design, and team mentorship are available as you gain experience. Your journey will be shaped by your contributions to advancing AI security and your ability to tackle complex challenges in this rapidly evolving field.
- How does Gray Swan AI foster collaboration between research and engineering teams?
- Gray Swan AI tightly integrates research and engineering. Your role as a Machine Learning Engineer will involve working at the boundary between these functions, ensuring that novel research ideas are directly translated into scalable, production-ready AI systems. Cross-functional collaboration is key to our approach, enabling research outcomes to directly inform and enhance our deployed production systems, ensuring real-world impact.
- What kind of real-world impact can a Machine Learning Engineer expect at Gray Swan AI?
- At Gray Swan AI, your work as a Machine Learning Engineer will have direct, tangible impact on AI safety. You'll be developing solutions that protect organizations from emerging AI security threats, influencing how leading AI companies deploy their models. This role offers the unique opportunity to apply advanced ML techniques to solve critical, real-world problems in AI security and robustness.
- How does Gray Swan AI support continuous learning for its Machine Learning Engineers?
- Gray Swan AI encourages continuous learning by immersing you in a team with leading minds in AI safety. You'll work with respected and influential thinkers, deepening your expertise in AI safety practices, adversarial testing, and model evaluation. The fast-paced startup environment also provides ample opportunities to learn by tackling novel challenges and staying at the forefront of AI security innovation.
- What opportunities exist for working with LLMs at Gray Swan AI?
- Experience with modern ML methods such as LLMs (training, finetuning, and/or analyzing) is highly desired at Gray Swan AI. As a Machine Learning Engineer, you will have opportunities to work with these advanced models, contributing to developing new approaches to adversarial testing, model evaluation, and robust inference for LLMs and other complex AI systems.