Senior Machine Learning Engineer, MLOps to help operationalize and scale our machine learning systems. This is an engineering-focused role centered on building the workflows, infrastructure, and processes that enable ML to move from research into reliable production systems.
Compensation
Competitive salary and equity in a high-growth startup
Responsibilities
Build and maintain the workflows and infrastructure that support the end-to-end ML lifecycle
Partner with researchers and ML practitioners to productionize models and enable faster iteration
Design, build, and improve data pipelines and training pipelines
Improve data processing, annotation workflows, and ML system efficiency
Deploy and maintain the background systems that support model training and inference
Senior ML Engineer (MSc. or PhD) contributing to AI training projects involving model development, training and inference systems, numerical computing, performance optimization, and Python.
Compensation
Pay: $60–$120/hour
Responsibilities
Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure.
Implement model components, data pipelines, evaluation systems, and numerical methods.
Build reproducible programmatic workflows using Python and command-line tools.
Requirements
Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline.
Lead the development of LLM inference infrastructure and architectural direction of large-scale ML training and real-time inference systems for Amazon Ads.
Locations (3)
Seattle, Washington, United States
Arlington, Virginia, United States
New York, New York, United States
Compensation
USA, CA, Palo Alto - 193,300.00 - 261,500.00 USD annually
Responsibilities
Lead the development of LLM inference infrastructure for next-generation GenAI workloads at Amazon Ads scale
Own the architectural direction of large-scale ML training and real-time inference systems
Partner with applied scientists to design and optimize end-to-end ML pipelines
Influence technical direction beyond your team by establishing ML infrastructure best practices
Provide SME support to the management of a DoD-wide training program, including coordinating with Combatant Commands to ensure USCYBERCOM-assigned forces are trained, certified, and interoperable with other Joint Forces.
Compensation
The projected compensation range for this position is $86,900.00 to $198,000.00 (annualized USD).
Responsibilities
Coordinate with Combatant Commands to ensure USCYBERCOM-assigned forces are trained, certified, and interoperable with other Joint Forces
Establish and maintain training and certification standards for the clients enabling USCYBERCOM operations
Assess the effectiveness of training to ensure training meets objectives, reinforces Joint standards, and facilitates the readiness assessment of cyberspace forces
Facilitate large working groups with internal stakeholders and external partners to establish and update baseline cyber training standards
Interpret and provide recommendations to senior leaders on national and higher headquarters joint doctrine, instructions, and directives pertaining to the Joint Training System (JTS)
Requirements
Bachelor’s degree
Nice to have: knowledge of cyberspace capabilities, equities, available infrastructure, or platforms
Nice to have: ability to work independently and with groups
Nice to have: excellent data analysis skills
Nice to have: excellent verbal and written communication skills
Design, develop, and deploy scalable AI/ML solutions across the healthcare domain.
Responsibilities
Design, develop, and deploy scalable AI/ML solutions across healthcare domain
Select, implement, and optimize supervised and unsupervised learning algorithms for classification, regression, clustering, anomaly detection, and forecasting
Learn and adopt AIDLC as the standard development approach, and promote its adoption through training and knowledge sharing
Develop deep learning, NLP, computer vision, recommender-system, reinforcement-learning, and generative AI models based on business needs
Perform feature engineering, hyperparameter tuning, cross-validation, error analysis, and model comparison using appropriate evaluation metrics
health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
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