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Senior Machine Learning Engineer

Senior Machine Learning Engineer

K2 Partnering Solutions
Fresher
Early Applicant
  • Posted a month ago
  • Be among the first 10 applicants

Job Description

Job Description & Requirements

We are seeking a Senior Member of Technical Staff – Machine Learning to drive the development of our core ML infrastructure and subsystems. In this role, you will lead end-to-end execution—from translating ambiguous requirements into practical designs to shipping scalable systems into production. This is a hands-on position requiring deep technical expertise and strong systems-level thinking.

Key Responsibilities

  • Architect, deploy, and maintain core ML systems powering long-horizon AI features.
  • Manage the full ML lifecycle, including data pipelines, model training, evaluation, inference, and continuous deployment.
  • Convert experimental research models into resilient, production-ready microservices.
  • Monitor, debug, and resolve complex production anomalies within strict latency, cost, and safety constraints.
  • Work cross-functionally with Research, Product, and Platform teams to deliver user-facing value.
  • Provide technical guidance, architectural oversight, and mentorship to junior and mid-level ML engineers.

Technologies: Python, PyTorch / JAX, Distributed GPU Training & Inference Workflows.

Qualifications

  • Demonstrated experience shipping and sustaining production ML systems with active user bases.
  • Strong mastery of production software engineering practices (modular design, testing, maintainability).
  • Deep familiarity with modern deep learning models, optimization techniques, and edge-case behavior.
  • Self-directed problem solver with excellent communication skills and an iterative approach to development.

Key Performance Indicators (KPIs)

  • System Reliability: Production ML services consistently meet or exceed performance, latency, and reliability benchmarks.
  • Operational Excellence: Fast resolution of production issues with minimal impact on service availability.
  • Business Alignment: ML initiatives deliver measurable improvements to core product metrics and business objectives.
  • Engineering Quality: Raised team standards through rigorous code reviews and impactful mentorship.

More Info

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Key Skills

Inference Workflows

Distributed GPU Training

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