Senior Machine Learning Engineer

Responsibilities

• Partner with scientists, engineers, and product managers to prototype AI research and turn promising technologies into products.
• Architect and develop infrastructure for training, evaluating, deploying, monitoring, and improving ML models across cloud and device environments.
• Work across model training and fine-tuning, large-scale evaluation, inference optimization, data pipelines, cloud infrastructure, and production ML systems.
• Build reusable AI platforms, infrastructure, and developer tooling for experimentation and productionization.
• Develop and operate generative AI systems using evaluation, fine-tuning, optimization, distillation, quantization, and scalable evaluation techniques.
• Solve edge-and-cloud ML challenges involving model quality, latency, reliability, privacy, and cost.
• Design secure and privacy-preserving AI systems and support responsible data use.
• Translate state-of-the-art ML research into robust engineering systems and new training, evaluation, and deployment techniques.
• Own technical problems end to end, influencing architecture and key technical decisions from experimentation through production.
• Establish engineering practices for ML architecture, testing, observability, reproducibility, and responsible AI.
• Mentor engineers, lead technical initiatives, and influence cross-team technical direction as the organization grows.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Electronics, Mathematics, or another highly technical field.
• At least 6 years of software engineering experience, including deploying AI/ML models to production in the cloud.
• Strong experience with cloud architecture and infrastructure as code.
• Proficiency in Python and C++, with experience using PyTorch or TensorFlow.
• Advanced hands-on knowledge of Linux environments and systems.
• Strong problem-solving, software architecture, and design skills focused on robust, scalable, maintainable systems.
• Ability to communicate and collaborate effectively with scientists, engineers, and product managers.
• Preferred: Master’s degree or PhD in a relevant technical field.
• Preferred: Experience with LLMOps, model evaluation, monitoring, quantization, distillation, and production deployment.
• Preferred: Hands-on experience fine-tuning and optimizing large-scale models.
• Preferred: Familiarity with model encryption, privacy-preserving machine learning, and secure AI techniques.

Benefits

• Competitive salary and 401k with employer match
• Discretionary paid time off
• Paid parental leave for all employees
• Medical, dental, and vision plans
• Fitness programs
• Emotional and mental wellness support
• Learning and development programs
• Hybrid work in Seattle, Washington, with onsite work Tuesday through Friday and remote flexibility on Monday