Sr Software Dev Engineer Sr Software Dev Engineer

Machine Learning Engineer 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 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 Quals-- 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): See below Ideal experience level (years, leadership, industries): 5-10+ years in large-scale ML systems Any preferred industries or companies for background? Large scale ML/AI companies (Google, Meta, Amazon, Microsoft) 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 EEO "Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans." Mindlance