About the Role: We are looking for a seasoned Principal Machine Learning Engineer to architect, build, and optimize ML inference platform. The role demands an individual with significant expertise in Machine Learning engineering and infrastructure, with an emphasis on building Machine Learning inference systems. Proven experience in building and scaling ML inference platforms in a production environment is crucial. This remote position calls for exceptional communication skills and a knack for independently tackling complex challenges with innovative solutions.
What you will be doing:
Architect and optimize our existing data infrastructure to support cutting-edge machine learning and deep learning models.
Collaborate closely with cross-functional teams to translate business objectives into robust engineering solutions.
Own the end-to-end development and operation of high-performance, cost-effective inference systems for a diverse range of models, including state-of-the-art LLMs.
Provide technical leadership and mentorship to foster a high-performing engineering team.
Requirements:
Proven track record in designing and implementing cost-effective and scalable ML inference systems.
Hands-on experience with leading deep learning frameworks such as TensorFlow, Keras, or Spark MLlib.
Solid foundation in machine learning algorithms, natural language processing, and statistical modeling.
Strong grasp of fundamental computer science concepts including algorithms, distributed systems, data structures, and database management.
Expert-level proficiency in at least one programming language such as Java, Python, or C++.
Ability to tackle complex challenges and devise effective solutions. Use critical thinking to approach problems from various angles and propose innovative solutions.
Worked effectively in a remote setting, maintaining strong written and verbal communication skills. Collaborate with team members and stakeholders, ensuring clear understanding of technical requirements and project goals.
Expertise in public cloud services, particularly in GCP and Vertex AI.
Must have:
Proven expertise in applying model optimization techniques (distillation, quantization, hardware acceleration) to production environments.
In-depth understanding of LLM architectures, parameter scaling, and deployment trade-offs.
Technical degree: Bachelor's degree in Computer Science with a minimum of 10+ years of relevant industry experience, or
A Master's degree in Computer Science with at least 8+ years of relevant industry experience.
A specialization in Machine Learning is preferred.
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About Rackspace TechnologyWe are the multicloud solutions experts. We combine our expertise with the world’s leading technologies — across applications, data and security — to deliver end-to-end solutions. We have a proven record of advising customers based on their business challenges, designing solutions that scale, building and managing those solutions, and optimizing returns into the future. Named a best place to work, year after year according to Fortune, Forbes and Glassdoor, we attract and develop world-class talent. Join us on our mission to embrace technology, empower customers and deliver the future.More on Rackspace TechnologyThough we’re all different, Rackers thrive through our connection to a central goal: to be a valued member of a winning team on an inspiring mission. We bring our whole selves to work every day. And we embrace the notion that unique perspectives fuel innovation and enable us to best serve our customers and communities around the globe. We welcome you to apply today and want you to know that we are committed to offering equal employment opportunity without regard to age, color, disability, gender reassignment or identity or expression, genetic information, marital or civil partner status, pregnancy or maternity status, military or veteran status, nationality, ethnic or national origin, race, religion or belief, sexual orientation, or any legally protected characteristic. If you have a disability or special need that requires accommodation, please let us know.
