Senior Machine Learning Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Assume operational responsibility for the machine learning and AI services deployed by Candid.

• Oversee service performance, manage retraining schedules, coordinate transitions from data scientists, and act as the primary contact for production models.

• Enhance inference efficiency for deployed models, including intricate graph inference models, through techniques such as quantization, artifact reduction, batching, and effective serialization.

• Design and manage systems for experiment tracking, model versioning, and artifact management.

• Develop and sustain observability for ML and AI services via centralized logging, metrics, dashboards, and alerts.

• Create and refine a repeatable AWS deployment process for new ML services, incorporating CI/CD integration and infrastructure-as-code methodologies.

• Track and manage AWS expenditures across ML workloads, including Bedrock token utilization, compute sizing, and S3 lifecycle management.

• Collaborate with data scientists to comprehend model behavior, extract operational insights, and convert research code into production-ready deployments.

• Act as a technical intermediary between Data Science and product/software engineering teams for ML and AI integration.

• Establish integration agreements, APIs, latency and reliability expectations, as well as input/output schemas.

• Develop and manage Amazon Bedrock-supported services and integrations.

• Contribute to secure-by-default ML services through IAM configurations, secrets management, and compliance-oriented tagging.

• Engage in technical planning and roadmap discussions.

⛳️ Requirements

• A minimum of 4 years of professional software engineering experience.

• At least 2 years of experience in MLOps, ML engineering, or data science with a focus on production ML systems as a core responsibility.

• Strong expertise in Python, including the development of production-quality service code.

• Practical experience with experiment tracking and model lifecycle tools such as MLflow or Weights & Biases.

• Hands-on experience in deploying PyTorch models in a production environment.

• Familiarity with quantization, batching, ONNX Runtime, model serialization, container/artifact optimization, or cold-start mitigation on Lambda/Fargate.

• Experience in deploying and monitoring ML models in production, including managing model degradation, drift signals, retraining triggers, and artifact oversight.

• Working knowledge of AWS ML deployment services, including Lambda, ECS/Fargate, S3, IAM, and CloudWatch.

• Proven experience in building or managing CI/CD pipelines for ML services.

• A history of enhancing production reliability through observability and disciplined deployment practices.

• Ability to collaborate closely with data scientists and articulate operational decisions effectively.

• Capability to work across different teams in software or product engineering.

• Comfortable taking ownership of work independently.

• Excellent written and verbal communication skills.

• Willingness to undertake additional duties and special projects as required.

• Awareness and respect for racial, gender, sexual orientation, and cultural diversity.

• Commitment to Candid's core values: driven, direct, accessible, curious, and inclusive.

🏝️ Benefits

• Health insurance (medical, dental, vision).

• Retirement savings plan with an additional matching option.

• Paid life insurance and accidental death & dismemberment coverage.

• Paid time off (PTO, compassionate leave, volunteer time, holidays, parental leave).

• Short-term and long-term disability insurance.

• Pre-tax transit benefits.

• Flexible spending accounts.

• Supplemental insurance options.

• Summer hours.

• Eligible employer for the Public Service Loan Forgiveness (PSLF) program.

• Remote work opportunities.

• Annual weeklong all-staff summits.