Senior Machine Learning Engineer

Deploy•Seattle, WA

11d

About The Position

Deploy is an AI infrastructure company providing developers and enterprises access to open-source and proprietary AI models. Its optimized inference engine helps run leading open-source large language models with low cost and latency, while its proprietary models address content safety, AI and deepfake detection, sponsorship intelligence, and intellectual property and publicity protection. Deploy has more than 250 employees across offices in San Francisco, Seattle, New York, and Delhi. The Senior Machine Learning Engineer will help develop and productionize machine learning models. The role involves prototyping state-of-the-art neural networks, working with large datasets, developing scalable data pipelines, and taking ownership of projects from development through production deployment and ongoing improvement. The position also contributes to product and backend systems, engineering practices, security, and the development of junior engineers. The expected base salary is $160,000–$250,000. Actual compensation may depend on qualifications, skills, competencies, experience, and location. Stock options may be offered. Company-sponsored benefits include health, vision, and dental insurance, a gym membership, and paid vacation in accordance with company policy.

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.
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 them at scale with high throughput and uptime; and analyze real-world results to continuously 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 suggesting and executing 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 relevant to the role.
Adhere to policies, guidelines, and procedures for protecting information assets.
Report actual or suspected security or policy violations or breaches to an appropriate authority.