Senior Machine Learning Infrastructure Engineer

Key Skills

Apache Airflow, Apache Kafka, AWS, Continuous Integration & Continuous Delivery - CI/CD, Docker, Effective communication skills, Github, Infrastructure as code, Jenkins, Kubernetes-K8s, Machine learning techniques, NVIDIA Jetson, Python Programming, PyTorch, Terraform

Job Description

The Role

We are seeking a highly skilled Senior Machine Learning Infrastructure Engineer to join our dynamic team. As a key member of our Engineering organization, you'll be instrumental in designing and building a robust infrastructure to support our suite of systems that fuel ML. This role involves a unique blend of technical expertise, innovative thinking, and a passion for data-driven solutions.

Your mission will be to connect the dots within the Metropolis ecosystem, encompassing hardware, machine learning, web, and mobile applications. You'll be responsible for developing and maintaining a scalable and sustainable infrastructure that underpins our ML models and data, ensuring the delivery of optimal solutions to our customers.

Responsibilities

• Design, implement, and maintain the machine learning infrastructure, including feature stores, data pipelines, and model serving platforms.
• Work with technologies like AWS, Kafka, Airflow, and NVIDIA Triton, ensuring efficient and effective use of these tools.
• Collaborate with other Engineering and Product teams to define requirements and facilitate the smooth deployment of software into production.
• Play a vital role in the entire development lifecycle, from concept and requirement identification to implementation.
• Bring your expertise into strategic decision-making such as whether to build or buy, fast and sloppy or slow and precise.
• Lead technical discussions, evaluate new technologies and methodologies, and drive continuous improvement in our ML infrastructure.
• Implement best practices in automation, testing, and deployment to foster rapid feature/model development and release.
• Foster a culture that views bugs and production issues as opportunities for improvement.
• Contribute to the hiring and cultural development within Metropolis Engineering.
• Ensure high availability and scalability of ML systems.

• Collaborate with partners to integrate their products seamlessly into the Metropolis platform.

Requirements and Qualifications

• MS or BS in Computer Science, Engineering, or a related field, or equivalent work experience.
• (Must have) At least 4 years of experience in software engineering, with a focus on ML infrastructure.
• (Must have) Proven experience in building and maintaining feature stores and data pipelines for ML models.
• (Must have) Familiarity with ML model serving infrastructure and best practices.
• (Must have) Proficiency in Python and strong experience with cloud services, preferably AWS.
• Knowledge of transactional and analytical database technologies.
• Knowledge of Terraform and experience with containerization technologies (e.g., Docker, Kubernetes).
• Experience in working with distributed systems, microservices, and event-driven architecture.
• Strong understanding of the agile development process and CI/CD pipelines and tools (e.g., Github Actions, Jenkins).
• Excellent communication skills, capable of presenting complex technical information clearly.
• Experience in training machine learning models and knowledge of frameworks (e.g., PyTorch) is a plus.
• Experience in high-growth, innovative environments is a plus.
• Preferred local to Seattle, Los Angeles (Hybrid).

When you join Metropolis, you’ll join a team of world-class product leaders and engineers, building an ecosystem of technologies at the intersection of parking, mobility, and real estate. Our goal is to build an inclusive culture where everyone has a voice and the best idea wins. You will play a key role in building and maintaining this culture as our organization grows. The anticipated base salary for this position is $120,000.00 to 190,000.00 annually. The actual base pay offered is determined by a number of variables, including, as appropriate, the applicant's qualifications for the position, years of relevant experience, distinctive skills, level of education attained, certifications or other professional licenses held, and the location of residence and/or place of employment. Base pay is one component of Metropolis’s total compensation package, which may also include access to or eligibility for healthcare benefits, a 401(k) plan, short-term and long-term disability coverage, basic life insurance, a lucrative stock option plan, bonus plans and more.