Zendesk

Senior Machine Learning Engineer

Zendesk

full-time

Posted on:

Location Type: Remote

Location: CaliforniaDistrict of ColumbiaUnited States

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Salary

💰 $196,000 - $294,000 per year

Job Level

About the role

  • Drive the design, development, and deployment of advanced ML and AI solutions, with an emphasis on large language models (LLMs), deep learning architectures, and sophisticated statistical modeling.
  • Build scalable, robust data science systems—from data ingestion, data curation, data modeling to algorithm development, model deployment and monitoring—meeting enterprise-grade performance, reliability, and compliance standards.
  • Act as a subject matter expert, collaborating with data scientists, ML engineers, analysts, and business stakeholders to understand needs, define requirements, and deliver practical solutions with measurable business impact.
  • Effectively articulate complex technical concepts to non-technical partners, bridging gaps between technical teams and business operations for maximum results.
  • Drive adoption of best practices in MLOps, including CI/CD pipelines, containerization, orchestration, observability, and reproducibility.
  • Oversee and enhance the integrity, security, and compliance of all data science workflows and contracts.
  • Stay abreast of the latest industry advancements in ML, LLMs, deep learning, cloud data engineering, and MLOps solutions (AWS, Kubernetes, Snowflake, etc.).
  • Fostering technical excellence and ensuring alignment with business objectives.

Requirements

  • 3+ years’ experience in Data Science, Machine Learning, or a related field
  • BA/BS in Computer Science, Data Science, or related discipline (advanced degree is highly preferred)
  • Deep expertise in statistical modeling, machine learning, and deep learning (including practical experience with LLMs and transformers)
  • Strong programming skills (Python preferred; Java, Scala, or similar also valued)
  • Proven ability to build and optimize scalable data science solutions—end-to-end—from data pipelines (dbt, Astronomer, Snowflake, AWS) to deployment and monitoring (Docker, Kubernetes, CI/CD, MLOps best practices)
  • Experience handling and analyzing large datasets, with a preference for experience in cloud data warehouses (Snowflake)
Benefits
  • Flexible working hours
  • Professional development opportunities
  • A fulfilling and inclusive experience
  • Health insurance
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
machine learningdeep learningstatistical modelingdata scienceprogramming (Python, Java, Scala)data pipelinesalgorithm developmentmodel deploymentMLOpsdata modeling
Soft Skills
collaborationcommunicationarticulation of technical conceptsproblem-solvingtechnical excellencestakeholder engagementbusiness impact focusadaptabilityteamworkleadership
Certifications
BA/BS in Computer ScienceBA/BS in Data Scienceadvanced degree (preferred)