Sr. Machine Learning Engineer

TEKsystems•Menlo Park, CA

5d•$70 - $85•Hybrid

About The Position

We're seeking a Senior Machine Learning Engineer to support the development of advanced AI systems capable of interacting with software, tools, and digital environments. This role focuses on evaluating model performance, identifying failure patterns, building benchmark frameworks, and delivering insights that directly improve model quality and training outcomes. You'll work closely with machine learning researchers, engineers, and cross-functional partners in a fast-moving environment where experimentation, analysis, and problem-solving are central to success.

Requirements

Bachelor's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related field
5+ years of hands-on experience developing with Python and SQL
3+ years of applied Machine Learning or ML Engineering experience
Experience evaluating machine learning models and analyzing performance metrics
Experience designing, implementing, or improving benchmarking and evaluation frameworks
Strong analytical and problem-solving skills with the ability to perform detailed failure analysis
Experience working with large-scale datasets and data pipelines
Comfortable working in Linux-based environments
Strong communication and collaboration skills

Nice To Haves

Experience evaluating Large Language Models (LLMs) or Generative AI systems
Exposure to Reinforcement Learning (RL) or Reinforcement Learning from Human Feedback (RLHF)
Experience building automated evaluation pipelines
Familiarity with agentic AI systems, AI assistants, or autonomous agents
Research experience, publications, or contributions to ML/AI projects

Responsibilities

Design, build, and maintain benchmark suites and evaluation frameworks for AI/ML models
Execute large-scale model evaluations and analyze performance across checkpoints
Investigate model failures and identify root causes behind poor performance
Develop Python and SQL workflows to process, transform, and analyze large datasets
Automate evaluation and reporting processes to improve efficiency and scalability
Partner with researchers and engineering teams to translate findings into model improvements
Document methodologies, experiments, and results
Communicate technical findings clearly to both technical and non-technical stakeholders