CentML

HQ
Toronto, Ontario, CAN
Total Offices: 2
50 Total Employees
Year Founded: 2022

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Benefits at CentML

Financial + Retirement

401(K)

Company equity

Performance bonus

Office Perks

Commuter benefits

Company-sponsored outings

Free daily meals

Onsite office parking

Relocation assistance

Free snacks and drinks

Health Insurance + Wellness

Dental insurance

Disability insurance

Flexible Spending Account (FSA)

Health insurance

Life insurance

Vision insurance

Mental health benefits

Culture

Flexible work schedule

Open door policy

Open office floor plan

Team based strategic planning

Employee-led culture committees

Day off for your birthday

Quarterly engagement surveys

Hybrid work model

In-person all-hands meetings

Child Care + Parental Leave

Generous parental leave

Vacation + Time Off

Generous PTO

Paid holidays

Paid sick days

Professional Development

Job training & conferences

Mentorship program

Paid industry certifications

Promote from within

Lunch and learns

Personal development training

Apprenticeship programs

Diversity

Hiring practices that promote diversity

Recently posted jobs

2 Days Ago
Hybrid
Toronto, ON, CAN
Machine Learning • Software
The Senior Software Engineer for LLM Inference will develop high-quality, efficient backend software, focusing on LLM inference innovation. Responsibilities include writing scalable code, conducting benchmarking and testing, and facilitating team collaboration through reviews and documentation.
2 Days Ago
Hybrid
Toronto, ON, CAN
Machine Learning • Software
The Software Engineer - Compiler role involves developing high-performance datacenter solutions for Deep Learning, optimizing GPU kernels, profiling ML/DL workloads, and writing tests and benchmarks to validate solutions. Strong coding skills in Python or C++, as well as experience with HPC/ML systems, are essential.
2 Days Ago
Hybrid
Toronto, ON, CAN
Machine Learning • Software
The Senior Software Engineer at CentML will focus on optimizing GPU kernels, profiling ML/DL workloads, and developing high-performance, power-efficient datacenter solutions for deep learning. Responsibilities include communication with product teams, understanding bottlenecks, and writing tests and benchmarks to validate solutions.