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
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)