Core ML Engineer: Deep Learning Architecture

Mecka • New York, NY

1d

About The Position

We're hiring an ML and Optimization Specialist to lead model architecture improvements across all of Mecka's pipelines.
Many of our current ML systems rely heavily on frame-by-frame models, but all of our data is inherently temporal. Your immediate focus will be converting and optimizing these models for temporal inference — a critical unlock for pipeline performance.
Beyond that, you'll be the go-to person for model-level debugging, architecture design, and optimization across the organization. This is a high-leverage, deeply technical role for someone who thinks at the architecture level.

Requirements

• Deep expertise in ML model architecture design and optimization
• Ability to tune and debug models at the architecture level — diagnosing issues in attention mechanisms, loss landscapes, gradient flow, etc.
• Strong experience with temporal/sequential models (transformers, RNNs, temporal convolutions, state-space models)
• Proficiency in PyTorch (or equivalent) at a research-engineering level
• Experience optimizing models for production deployment

Nice To Haves

• Published papers or production experience with video understanding or temporal perception
• Experience with model distillation, quantization, or efficient inference
• Background in computer vision model architectures
• Experience converting or adapting pre-trained models to new domains/modalities
• Familiarity with ONNX, TensorRT, or similar inference optimization tools

Responsibilities

• Temporal model conversion — migrate frame-by-frame models to temporal architectures that leverage sequential data
• Benchmark and validate temporal models against existing frame-based baselines
• Lead model architecture improvements across all pipelines (CV, pose estimation, etc.)
• Tune and debug ML models at the model architecture level — not just hyperparameters, but structural decisions
• Profile and optimize model performance (latency, throughput, memory)
• Evaluate and introduce new architectures, training strategies, and optimization techniques
• Collaborate with CV, ML, and infrastructure teams to deploy improved models

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed