Software Dev Engineer/ML Ops

Job title: Sr Software Dev Engineer/ML Ops

Duration: 12 months Contract

Location: Remote

Qualifications:

How does this role fit within the team/department?

This role sits within core MLOps and ML infra systems that enable researchers and engineers. Focus is on ensuring scalable, efficient, and standardized ML workflows.

Overview of the team:

Platform team enabling research + engineering via shared ML systems and platforms.

Key team goals:

Scalable ML workflows and pipelines, ML infra, efficient automation of ML workflows, robust data sampling / feature generation platforms, standardized training and deployment, GKE training and serving infra, knowledge distillation pipelines, foundational training tools

Must-have skills/qualifications (technical, soft skills, certifications, tools):

Ideal experience level (years, leadership, industries)

5-10+ years in large-scale ML systems

Responsibilities:

Primary responsibilities (daily/weekly):

Scalable ML workflows and pipelines,

ML infra,

efficient automation of ML workflows,

robust data sampling / feature generation platforms,

standardized training and deployment

GKE training and serving infra,

knowledge distillation pipelines,

foundational training tools

Desired personality or work style:

Ownership-driven, pragmatic, fast-moving, strong collaborator with research + eng

Key attributes or values sought in the candidate:

Reliability and consistency focus, scalability mindset, cost awareness, methodical approach

Challenges in hiring for this role in the past (if applicable):

Candidate needs to have deep ML + mlops/infra + large scale + distributed systems expertise

What has worked well in hiring for similar roles?

Prioritizing hands-on MLEs with proven large-scale ML platform experience

Any additional details or red flags to note about the role or candidate?

Avoid pure DevOps without ML training experience or pure theoretical profiles

Key projects or initiatives for the role:

Scalable ML workflows and pipelines, ML infra, efficient automation of ML workflows, robust data sampling / feature generation platforms, standardized training and deployment, GKE training and serving infra, knowledge distillation pipelines, foundational training tools

Success metrics or KPIs for this role:

Time to market, Training efficiency, infra uptime, cost optimization, pipeline reliability, onboarding speed

How is success measured?

Time to market, Faster experimentation cycles, reduced cost, stable deployments, high platform adoption