We are building the next-gen AI inference platform.
DescriptionLocation: Seattle, WA (Hybrid - 3 days/week in office)
About ElastixAI:
ElastixAI is an early-stage startup poised to revolutionize AI inference infrastructure. We are developing a cutting-edge AI inference solution that dramatically improves efficiency. Our solution is going to dynamically adapt to any deployment, constantly evolving to power the next generation AI use cases.
Role Summary:
We are looking for a talented Machine Learning Engineer to play a key role in building our core AI inference platform. You will be responsible for designing and developing critical components, including ML model deployment, innovative model optimization pipelines, and performance benchmarking and simulation workflows. This is a highly interdisciplinary role where you'll collaborate closely with our multi-disciplinary team to ensure our entire stack works in harmony to deliver optimal AI inference solutions.
Key Responsibilities:
Design, develop, and maintain core components of our ML platform, focusing on scalability, reliability, and ease of use.
Research, prototype, and implement advanced ML techniques to optimize inference performance across diverse hardware targets.
Collaborate with systems and cloud engineers to ensure efficient utilization of underlying hardware resources.
Contribute to the design of APIs and tools that enable seamless integration and management of our inference solutions.
Required Qualifications:
PhD/MS in Computer Science, Computer Engineering, or a related field.
3+ years of machine learning R&D and deployment experience.
Strong proficiency in one or more programming languages such as Python, or C++.
Proficiency in at least one ML framework (e.g., PyTorch, TensorFlow, JAX).
Solid understanding of software engineering best practices, including data structures, algorithms, and testing.
Excellent problem-solving abilities and a knack for tackling complex technical challenges.
Strong communication skills and a proven ability to collaborate effectively in a cross-functional team environment.
Ability to thrive in a fast-paced, dynamic startup environment.
Preferred/Bonus Qualifications:
Experience with training generative machine learning models.
Experience with optimizing the performance of machine learning models.
Experience leading research initiatives in Machine Learning, Natural Language Processing, and related fields.
Familiarity with performance analysis, profiling, and optimization techniques.
Experience with cloud platforms (AWS, GCP, Azure).
Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
What We Offer:
A chance to be a foundational engineer in an innovative AI startup.
A dynamic and collaborative work environment and the change to have a significant impact on new technology
The opportunity to work on challenging problems at the intersection of ML, software, and systems.
Competitive compensation and startup equity package
Comprehensive medical, dental, and vision coverage (100% paid by employer)
Flexible Time Off (FTO)
Paid parental leave
Gym or fitness benefit
Commuter benefit
Investment in employee learning & development
Skills Required
- PhD or MS in Computer Science, Computer Engineering, or related field
- 3+ years of machine learning R&D and deployment experience
- Proficiency in one or more programming languages such as Python or C++
- Proficiency in at least one ML framework (PyTorch, TensorFlow, or JAX)
- Solid understanding of software engineering best practices, data structures, algorithms, and testing
- Excellent problem-solving abilities and ability to tackle complex technical challenges
- Strong communication skills and ability to collaborate in cross-functional teams
- Ability to thrive in a fast-paced, dynamic startup environment
- Experience with training generative machine learning models
- Experience optimizing machine learning model performance
- Experience leading research initiatives in ML, NLP, or related fields
- Familiarity with performance analysis, profiling, and optimization techniques
- Experience with cloud platforms (AWS, GCP, Azure)
- Experience with containerization and orchestration (Docker, Kubernetes)
What We Do
ElastixAI delivers elastic, cost-efficient AI inference by co-designing machine learning models, system software, and reconfigurable hardware as a unified architecture. Their mission is to enable adaptable and cost-efficient GenAI inference infrastructure, driving breakthroughs and making Artificial Super Intelligence accessible to everyone. By removing inefficiencies at every layer of the stack, they achieve lower total cost of ownership and reduced power consumption per token.
