Machine Learning Engineer

Designing and implementing end-to-end machine learning pipelines from data preparation through model deployment

Training and fine-tuning large language models for specific tasks and domains

Optimizing machine learning models for inference efficiency and latency constraints

Integrating and deploying machine learning models into real-world systems and applications

Building and scaling distributed training infrastructure across multiple GPUs and compute nodes

Writing high-performance production code with strong software engineering fundamentals

Implementing reinforcement learning pipelines and reward modeling for model improvement

Designing data pipelines for sourcing, processing, filtering, and preparing training corpora at scale

Evaluating model performance through rigorous benchmarking and evaluation methodologies

Implementing post-training techniques including preference optimization and alignment

Developing multi-modal machine learning models that process images, video, and text

Architecting systems for real-time inference with strict latency and memory constraints

Designing and executing ablation studies to validate machine learning design decisions

Handling and processing large-scale unstructured data including images, video, and sensor streams

Profiling and identifying bottlenecks across the full machine learning stack

Building computer vision models for perception tasks including object detection and semantic understanding

Building observability and monitoring systems for production machine learning pipelines

Managing and optimizing cloud computing resources for machine learning workloads

Debugging complex distributed systems to identify root causes of performance degradation

Developing simulation environments for testing and validating autonomous systems

Implementing low-level optimizations including GPU kernel optimization and memory management

Understanding and optimizing communication primitives and synchronization across distributed systems

Processing and analyzing three-dimensional geometric data including point clouds and meshes

Designing agentic systems that orchestrate multiple models and decision-making components

Implementing knowledge distillation to transfer capabilities from large to smaller models

Collaborating with researchers to translate theoretical concepts into production implementations

Working cross-functionally with product, design, and engineering teams to deliver business impact

Staying current with machine learning research and rapidly incorporating new techniques

Defining clear technical roadmaps and breaking down ambiguous research problems into measurable milestones

Mentoring and guiding junior engineers in machine learning practices and technical problem-solving