Senior Machine Learning Engineer

Hive•San Francisco, CA

8d•$160,000 - $250,000

About The Position

Deploy is an intelligence infrastructure platform providing access to open-source and proprietary AI models. Its services include optimized inference for leading open-source LLMs and proprietary models for content safety, AI and deepfake detection, sponsorship intelligence, and intellectual-property and publicity protection. The company has more than 250 employees across offices in San Francisco, Seattle, New York, and Delhi. The Senior Machine Learning Engineer will help build and productionize machine-learning models, staying current with deep-learning technology and prototyping state-of-the-art neural networks. The role covers the end-to-end application of machine-learning models to production use cases, including data preparation, model development and training, deployment, and ongoing performance analysis and improvement. The position also contributes to product and backend systems, engineering standards, security, research, and the development of junior engineers.

Requirements

Bachelor's degree in computer science or a related field.
At least 5 years of experience building production-scale machine-learning models.
Knowledge of modern machine-learning frameworks such as PyTorch or TensorFlow.
Expertise in scripting languages such as Python and/or shell scripts, particularly for data analysis.
Experience writing code and training across distributed systems.
Ability to understand and make well-reasoned tradeoffs when designing features.
Ability to lead end-to-end development of new products.
Strong knowledge in at least one machine-learning focus area, such as computer vision or NLP.
Strong commitment to high code quality, automated testing, and other engineering best practices.
Attention to detail and a passion for correctness.
Comfort with ambiguity and scoping solutions with teammates.
Strong interpersonal and communication skills, with a bias toward action.

Responsibilities

Design and code neural networks; gather and refine data; train and tune models; deploy models at scale with high throughput and uptime; and analyze real-world results to continually improve accuracy and speed.
Write and maintain scalable, performant, and secure code that can be shared across platforms.
Contribute to product and core backend systems by proposing and implementing improvements.
Improve engineering standards, tooling, processes, and security.
Develop novel, accurate, and performant machine-learning algorithms for use at scale.
Conduct metric-driven research experiments to improve model performance.
Mentor and help onboard junior machine-learning engineers.
Collaborate cross-functionally with other teams.
Use OWASP Top 10 techniques to secure code from vulnerabilities.
Maintain awareness of industry best practices for data maintenance and handling as they relate to the role.
Follow policies, guidelines, and procedures for protecting information assets.
Report actual or suspected security or policy violations and breaches to an appropriate authority.