Senior Machine Learning Engineer, ML Efficiency

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer, ML Efficiency based in United States.

This role offers the opportunity to shape the future of machine learning efficiency within a large-scale technology environment. You will focus on improving the speed, cost, reliability, and scalability of advanced ML systems powering critical products. The position sits at the intersection of machine learning, systems engineering, and performance optimization. You will own high-impact initiatives across model training, inference, serving infrastructure, and operational readiness. Working closely with engineering and platform teams, you will identify bottlenecks and develop reusable solutions that drive measurable improvements. This is an opportunity for a senior engineer to influence technical strategy, mentor peers, and build systems that enable broader engineering impact.

Accountabilities:: As a Senior Machine Learning Engineer focused on ML Efficiency, you will lead complex optimization initiatives designed to improve production ML workloads. You will combine deep technical expertise with strong engineering judgment to identify performance challenges, implement scalable solutions, and establish best practices that improve efficiency across teams.

Own high-value optimization projects across machine learning training, inference, serving systems, and launch-readiness workflows.

Diagnose performance bottlenecks in production environments using profiling, benchmarking, monitoring, and observability tools.

Develop optimization frameworks, performance tooling, efficiency playbooks, and reusable engineering capabilities that benefit multiple teams.

Improve model launch readiness by contributing to load testing, reliability improvements, fallback strategies, latency monitoring, and cost visibility.

Partner with applied ML engineers, platform teams, and infrastructure owners to implement practical solutions while maintaining scalability and maintainability.

Influence technical direction by identifying recurring challenges, automation opportunities, and patterns that can be standardized across engineering teams.

Mentor engineers by sharing expertise in debugging, measurement-driven development, system optimization, and technical execution.

Requirements:

The ideal candidate is an experienced machine learning systems engineer with a strong background in improving the efficiency of real-world ML workloads. You should be comfortable working across modeling, runtime, and infrastructure layers while collaborating with multiple technical teams.

Deep experience working with production machine learning systems and large-scale ML workloads.

Proven track record of improving ML training or inference efficiency with measurable performance outcomes.

Strong understanding of optimization strategies across model-level, runtime-level, and infrastructure-level systems.

Experience owning complex technical projects from concept through implementation and impact measurement.

Ability to balance short-term performance improvements with long-term scalability, adoption, and maintainability.

Strong communication skills with the ability to explain complex technical tradeoffs clearly to engineering and partner teams.

Experience with GPU training or serving optimization is preferred.

Experience with PyTorch, distributed training frameworks, or kernel/runtime optimization is a plus.

Familiarity with efficiency benchmarking, launch certification systems, cost monitoring, or model deployment optimization techniques such as quantization, pruning, distillation, or checkpoint optimization is beneficial.

Benefits:

Comprehensive healthcare benefits and income replacement programs.

401(k) program with employer match.

Global benefits designed to support professional development, caregiving, and flexible work needs.

Family planning support.

Gender-affirming healthcare coverage.

Mental health and coaching benefits.

Flexible vacation policy and paid volunteer time off.

Generous paid parental leave.

Competitive compensation package including base salary, equity opportunities, and additional incentives depending on role scope.

Estimated base salary range: $216,700 - $303,400 USD .

How Jobgether works: We use an AI-powered matching process 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!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1