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Core ML Engineer - Deep Learning Architecture

mecka.aiNew York
On-site Full-time

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Experience Level

Mid to Senior

Qualifications

Who You AreRequired SkillsExtensive expertise in ML model architecture design and optimization. Proficient in tuning and debugging models at the architecture level—capable of diagnosing issues in attention mechanisms, loss landscapes, gradient flow, etc. Strong background in temporal/sequential models (transformers, RNNs, temporal convolutions, state-space models). Proficient in PyTorch (or equivalent) at a research-engineering level.

About the job

The Opportunity

Join us at mecka.ai as a Core ML Engineer specializing in Deep Learning Architecture. In this pivotal role, you will spearhead enhancements to our model architecture across all pipelines.

This position emphasizes foundational deep learning engineering over applied machine learning. We seek an engineer adept at creating, debugging, and refining internal model architectures from the ground up, moving beyond mere utilization of pre-existing models or standard fine-tuning.

Our current ML systems predominantly utilize frame-by-frame models, yet our data is inherently temporal. Your primary responsibility will be transforming and optimizing these models for temporal inference—a crucial factor in unlocking pipeline performance.

Additionally, you will become the key resource for model-level debugging, architecture design, and optimization throughout our organization. This is a high-impact, deeply technical role suited for someone who possesses a keen architectural mindset.

Key Responsibilities

Immediate Focus

  • Temporal Model Conversion: Transition frame-by-frame models to temporal architectures that harness sequential data.
  • Benchmark and validate temporal models against existing frame-based baselines.

Ongoing Tasks

  • Lead enhancements in model architecture across all pipelines (e.g., CV, pose estimation).
  • Tune and debug ML models at the architecture level—modify structural code, craft custom layers, and address the fundamental mathematics rather than relying solely on high-level APIs or hyperparameter tuning.
  • Profile and optimize model performance metrics, including latency, throughput, and memory usage.
  • Assess and integrate new architectures, training strategies, and optimization techniques.
  • Collaborate with CV, ML, and infrastructure teams to deploy enhanced models.

About mecka.ai

At mecka.ai, we are committed to pioneering advancements in machine learning and optimization. Our innovative team is dedicated to transforming the landscape of AI technology with cutting-edge tools and methodologies.

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