About the Company The company is developing next-generation AI systems designed to deliver reliable, real-world experiences at scale. Its focus is on moving machine learning beyond experimentation and building the production infrastructure needed to deploy, evaluate, monitor, and continuously improve advanced AI capabilities.
About the Role The company is seeking an ML Engineer to bridge machine learning and software engineering, with a focus on building and maintaining production-ready machine learning infrastructure. This role involves developing scalable systems for model execution, automating machine learning workflows, and improving the reliability and performance of models in production.
You will work closely with machine learning, research, product, and software engineering teams to turn evolving technical requirements into dependable solutions. The position offers ownership across the machine learning lifecycle, with opportunities to contribute to system architecture, engineering practices, and the technical direction of the ML environment.
Key Responsibilities
•Engineer reliable production infrastructure to support machine learning workloads from development through deployment and ongoing operation.
•Build scalable model execution services that meet demanding latency and throughput requirements.
•Develop automated workflows for data preparation, model validation, deployment, and iterative improvement.
•Create evaluation frameworks that help teams assess model behavior, track performance changes, and identify quality regressions.
•Implement monitoring and diagnostic tools to provide visibility into the health and behavior of production ML workloads.
•Identify performance bottlenecks across the machine learning lifecycle and improve speed, capacity, resilience, and infrastructure efficiency.
•Collaborate with machine learning, research, product, and software engineering teams to develop robust production solutions.
•Build reusable infrastructure capabilities that reduce duplicated work and support faster development.
•Maintain an adaptable ML environment that can accommodate new model architectures, serving methods, and infrastructure techniques.
Required Qualifications
•Strong software engineering fundamentals, with demonstrated experience developing and supporting production-grade systems.
•Hands-on experience building ML infrastructure, shared machine learning capabilities, or machine learning applications used in production.
•Practical experience in at least one area of machine learning infrastructure, such as model serving, inference, evaluation systems, deployment workflows, or ML data pipelines.
•Strong understanding of distributed computing, fault tolerance, operational reliability, and scalable system design.
•Ability to write clean, maintainable, well-structured code for long-term production use.
•Strong analytical and troubleshooting skills, including the ability to investigate system bottlenecks and improve performance.
•Ability to work through technical ambiguity and changing requirements while taking ownership of problems from investigation to implementation.
Preferred Qualifications / Requirements
•Experience with Python.
•Exposure to machine learning frameworks such as PyTorch or JAX.
•Familiarity with cloud environments, workflow orchestration, GPU-based computing, or high-performance model serving.
•Exposure to technologies used for large language model (LLM) inference, retrieval systems, or vector-based data architectures.
Salary & Benefits
Compensation is individually structured and includes cash and equity. Benefits include health, dental, and life insurance options, along with a 401(k) match. The role offers ownership of production machine learning systems and the opportunity to contribute to architecture, engineering practices, and technical direction.
Location & Work Arrangement
•Location: Remote, nationwide.
Equal Opportunity / EEO
No specific Equal Opportunity or non-discrimination statement was included in the original job listing.