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Senior Machine Learning Engineer, ML Efficiency
Rol remoto de Ads Engineering con fit claro de ubicación del candidato.
PublicadoAgregado recientemente
Países elegibles1 país aceptado
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Estados Unidos
Resumen del rol
Senior Machine Learning Engineer, ML Efficiency
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
What you’ll do
- Independently own high-value optimization initiatives across training, inference, or launch-readiness for important Ads ML workloads.
- Diagnose bottlenecks in real production systems using profiling, benchmarking, and observability rather than intuition-first debugging.
- Build performance tooling, optimization playbooks, observability hooks, guardrails, or efficiency primitives that help more than one team or workload over time.
- Improve launch-safety and efficiency readiness by contributing to load testing, fallback readiness, latency and cost visibility, and operational confidence for heavy models.
- Work with model owners and platform teams to land pragmatic fixes while helping the team gradually standardize repeated solutions.
- Contribute to the team’s technical direction by surfacing patterns, tradeoffs, and opportunities for reuse or automation.
- Mentor less-experienced engineers through code, debugging, measurement rigor, and strong execution habits.
What we’re looking for
- Deep ML systems experience close to real production models and workloads, not just generic infra exposure.
- Direct hands-on experience improving training or serving efficiency with measurable outcomes.
- Strong technical judgment across model-level, runtime-level, and infrastructure-level optimization choices.
- Ability to own complex projects end to end and collaborate effectively across team boundaries.
- Good customer and platform instincts: can solve concrete bottlenecks while keeping maintainability, adoption, and future reuse in mind.
- Strong communication: able to explain tradeoffs clearly to engineers and partner teams.
Nice-to-have
- Experience with GPU training or serving migrations.
- Experience with PyTorch, distributed training frameworks, or kernel/runtime optimization.
- Experience building launch certification, efficiency benchmarking, or cost observability systems.
- Experience in organizations where platform and applied modeling responsibilities are split across multiple teams.
- Experience with model compression or deployment optimizations such as quantization, pruning, distillation, or checkpoint optimization.
Nice-to-have
- Comprehensive Healthcare Benefits and Income Replacement Programs
- 401k with Employer Match
- Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
- Family Planning Support
- Gender-Affirming Care
- Mental Health & Coaching Benefits
- Flexible Vacation & Paid Volunteer Time Off
- Generous Paid Parental Leave
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Flujo de contratación
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