Scan your resume against ATS criteria for this Lead Machine Learning Engineer - ML Infrastructure role at jobgether.
This role serves as the technical anchor for machine learning infrastructure supporting large-scale AI products and applications.
You will own the architecture and evolution of an end-to-end ML platform spanning training, experimentation, inference, and edge deployment.
The position connects applied machine learning, data platform, security, firmware, and infrastructure teams around shared technical direction.
You will make high-impact architectural decisions that influence multiple product teams and production systems at significant scale.
The role combines hands-on engineering with technical leadership, reliability, security, developer experience, and operational ownership.
You will help deliver ML-powered capabilities from initial design through production deployment while balancing performance, latency, cost, and research velocity.
This is a senior technical leadership opportunity where your decisions can directly influence real-world safety, efficiency, and customer outcomes.
Set the technical strategy and own end-to-end delivery of the machine learning platform across training, experimentation, batch and inference, and edge deployment.
Make architectural decisions across ML infrastructure layers and serve as the primary technical accountability point for multiple AI product teams.
Design, launch, and continuously improve ML-powered features while co-owning production outcomes such as safety metrics, reliability, performance, and cost.
Design and operate scalable and batch inference systems using technologies such as
, including deployment patterns, observability, service-level objectives (SLOs), and unified training-to-production workflows.
Partner with firmware and edge engineering teams to package, validate, and deploy machine learning models to connected devices.
Build feedback loops between edge deployments and cloud infrastructure to support continuous model and system improvement.
Own reliability, observability, security, and operational practices for ML systems spanning cloud and edge environments.
Establish and improve on-call practices, incident response processes, infrastructure hardening, and production reliability standards.
Own or co-own end-to-end technical delivery for high-priority and high-risk initiatives, from modeling and system architecture through production rollout.
Serve as the technical authority for ML infrastructure architecture and establish direction for applied ML, firmware, security, and data platform teams.
Mentor senior engineers and applied scientists while helping teams make sound technical trade-offs at the appropriate level of abstraction.
Improve developer experience through documentation, engineering standards, reusable practices, and clear platform guidance.
Contribute to and represent the organization within relevant open-source communities, including
.
Balance research velocity with platform stability and communicate technical trade-offs effectively across science and engineering teams.
Champion customer-focused, long-term, inclusive, collaborative, and growth-oriented engineering practices.
, with demonstrated technical leadership across at least two major ML platform domains such as distributed training, data or research infrastructure, cloud inference, or feature engineering.
Proven track record of delivering ML-powered products or features end-to-end, from technical design through production deployment and iteration, with measurable product or business impact.
Strong hands-on expertise with
in production environments.
is highly preferred.
Deep understanding of machine learning fundamentals beyond pipelines, including evaluation methodology, dataset design, ablation, model drift, and the ability to review and redirect modeling approaches.
Ability to bridge research and engineering teams and translate ML requirements into scalable, reliable production systems.
Demonstrated cross-organizational technical leadership, including influencing platform decisions, roadmaps, and go/no-go decisions based on throughput, latency, reliability, and cost trade-offs.
Strong understanding of distributed ML infrastructure and production systems at scale.
Experience navigating the trade-offs between scientific experimentation and platform stability, with the communication skills needed to align both sides.
Strong architectural judgment and ability to operate as the senior technical authority for complex ML infrastructure initiatives.
Excellent communication and collaboration skills, with the ability to influence senior technical stakeholders without relying solely on formal authority.
Strong mentoring and technical leadership capabilities.
Prior contributions to open-source projects such as
are a plus.
Experience with enterprise security and compliance requirements in ML environments is a plus.
Experience with edge or on-device machine learning and collaboration with firmware or embedded engineering teams is a plus.
, with actual compensation varying based on factors such as location, knowledge, skills, and experience.
, subject to applicable plan terms.
Ongoing equity refresh opportunities tied to performance, subject to plan terms and conditions.
Performance-based bonus or variable compensation for eligible roles.
Flexible, employee-led remote working model.
Comprehensive health benefits.
Parental leave programs.
Professional development stipend.
Opportunities to work on high-impact AI and machine learning infrastructure at significant scale.
Opportunity to influence platform architecture and technical direction across multiple product teams.
Exposure to cloud, edge, distributed computing, machine learning, and open-source technologies.
Collaborative environment emphasizing long-term ownership, growth, inclusion, and customer outcomes.
Reasonable accommodations available throughout the recruitment process for qualified candidates.
to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
https://jobgether.com/how-jobgether-works Why Apply Through Jobgether?
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, with demonstrated technical leadership across at least two major ML platform domains such as distributed training, data or research infrastructure, cloud inference, or feature engineering.
in production environments.
is highly preferred.
are a plus.
Benefits
, with actual compensation varying based on factors such as location, knowledge, skills, and experience.
How to apply for Lead Machine Learning Engineer - ML Infrastructure at jobgether?
Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.
What is the salary for this role?
The salary for this role is $196,000 per annum.
What experience is required?
10+ years of experience is required.
Is this position still open?
Yes, currently active and accepting applications.
Lead Machine Learning Engineer - ML Infrastructure
jobgether · Canada
