3 hours ago
Base Salary
$151k - $241k/yr
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