# Machine Learning Engineer at Elastix > Hybrid · United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Aelastix%3Ab373615d-5e66-495c-9928-0cc0e0043602 (read 2026-11-12). ## Facts (observed) - Company: [Elastix](https://alderhq.app/companies/elastix) - Location text: Seattle - Where it can be done from (inferred): Hybrid · United States (evidence: "Location: Seattle") - Posted: 2026-02-19 (266 days ago) - First seen by alder: 2026-11-04 · last verified: 2026-11-08 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/elastix/b373615d-5e66-495c-9928-0cc0e0043602 - Skills listed (inferred): Python, C++, AWS, GCP, Azure, Kubernetes, Docker, PyTorch, TensorFlow ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (Seattle, n 16): p25 $161k, median $183k, p75 $192k ## Description (the employer's text) ### About Elastix AI We are building the next-gen AI inference platform. ### Description Location: 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 --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aelastix%3Ab373615d-5e66-495c-9928-0cc0e0043602 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.