
Senior Machine Learning Engineer
Zendesk
full-time
Posted on:
Location Type: Remote
Location: California • District of Columbia • United 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)