Machine Learning Engineer
$160k - $257k • Pittsburgh • Full-time
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Gray Swan AI is seeking a Machine Learning Engineer to play a pivotal role in shaping the future of AI safety solutions. This role blends applied research with system building, focusing on developing new approaches to adversarial testing, model evaluation, and robust inference. You will work at the boundary between research and production, translating novel ideas into scalable AI systems that withstand adversarial pressure. Your expertise in deep learning architectures, distributed systems, and parallel computing will be crucial for tackling complex challenges associated with resource-intensive models and advancing methodologies for controlling, monitoring, and analyzing these models in production environments.
Responsibilities
- Lead the design, development, and deployment of advanced machine learning models to enhance system performance and scalability.
- Address complex challenges with resource-intensive models using distributed systems and parallel computing.
- Advance methodologies for controlling, monitoring, and analyzing machine learning models in production.
- Develop new approaches for adversarial testing, model evaluation, and robust inference.
- Translate research ideas into scalable AI systems for real-world, adversarial settings.
- Collaborate with cross-functional teams to ensure research outcomes inform production systems.
Requirements
- Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical field.
- Experience building and deploying machine learning models and systems.
- Expertise in designing, training, and deploying deep learning models with frameworks like PyTorch.
- Strong programming experience in Python and C++ (preferred).
- Experience developing scalable machine learning pipelines and integrating them with cloud infrastructure (e.g., AWS, GCP, Azure).
- Experience conducting ML research, including building research prototypes, experiment design, empirical analysis, and communicating results.
- In-depth knowledge of neural network architectures, including sequence models and transformers.
- Strong algorithmic problem-solving skills and knowledge of ML theory and optimization techniques.
- Proficiency in data preprocessing, transformation, and handling large-scale, multi-modal datasets.
Benefits
- Performance-based bonus
- Competitive equity package
- 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