Production ML Engineer: Scalable Models & Pipelines

Hive; Seattle (WA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Vision insurance

Dental insurance

Gym membership

Paid vacation

Job summary

Deploy is building an intelligence infrastructure platform and seeks a machine learning engineer to prototype state‑of‑the‑art models and bring them into production at scale in Seattle.

You will work across data pipelines, model deployment, and performance tuning, collaborating with cross‑functional teams to deliver secure, reliable ML solutions. A strong background in Python and DL frameworks will be essential.

Qualifications

• Undergrad or grad degree in CS or similar with strong math/stat coursework.
• 1–2 years of industry ML experience.
• Have successfully trained and deployed a deep learning model into production with measurable performance gains.
• Strong experience with high‑level ML frameworks such as TensorFlow, Caffe, or Torch, and familiarity with others.
• Proficient in Python for ML workflows.
• Ability to prototype data pipelines involving Python, Node, Bash, and Linux CLI over very large datasets.
• Working knowledge of C++, Scala/Spark, SQL, Cassandra, Docker.
• Up-to-date on recent DL research and architectures and how to implement them.
• Excellent communication and teamwork.

Responsibilities

• Apply ML models to production use cases, including designing, coding, training, deploying, and monitoring performance.
• Collaborate with Backend/DevOps and data labeling teams.
• Use OWASP top 10 to secure code and protect information assets.
• Maintain awareness of data maintenance best practices for ML projects.
• Follow policies on information protection and report violations when needed.

Skills

Python

TensorFlow

Caffe

Torch

Scala / Spark

SQL

C++

Docker

Education

Bachelor's or Master's in Computer Science

Tools

Linux

Docker

Spark

Cassandra

Job description

Deploy is building an intelligence infrastructure platform and seeks a machine learning engineer to prototype state‑of‑the‑art models and bring them into production at scale in Seattle.

You will work across data pipelines, model deployment, and performance tuning, collaborating with cross‑functional teams to deliver secure, reliable ML solutions. A strong background in Python and DL frameworks will be essential.