Core ML Engineer: Deep Learning Architecture

Mecka•New York, NY

About The Position

We're hiring an ML and Optimization Specialist to lead model architecture improvements across all of Mecka's pipelines. This role is heavily focused on foundational deep learning engineering rather than applied ML. We are looking for an engineer who natively writes, debugs, and modifies internal model architectures from the ground up, moving beyond utilizing off-the-shelf models or standard fine-tuning. 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 — modifying structural code, writing custom layers, and addressing the underlying math, rather than relying solely on high-level APIs or hyperparameter tuning
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