Core ML Engineer: Deep Learning Architecture

The Role

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.
Responsibilities
Immediate Priorities

• 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

Ongoing

• 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

Who You Are
Required Skills

• 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

Strong Signals

• 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

You Are

• Obsessed with model internals - you think in terms of structural architecture and custom implementations, rather than just training runs and applied endpoints
• Able to move between research papers and production code
• A strong communicator who can explain architecture tradeoffs to cross-functional teams

Why This Role

• Own the model architecture strategy across all of Mecka's pipelines
• Solve a critical temporal modeling challenge with immediate impact
• Work at the intersection of perception, robotics, and ML systems
• High ownership in a fast-moving, well-funded robotics AI company