Note: The job is a remote job and is open to candidates in USA. Grid Dynamics is a technology consulting and product engineering company providing AI, advanced analytics, and related technology services. The Senior ML Engineer will own machine learning projects, develop evaluation methodologies and infrastructure, and improve LLM-based systems including RAG applications, agents, and safety systems. The role also involves analyzing model behavior, building ML pipelines, collaborating across teams, and communicating recommendations to stakeholders.
Responsibilities
- Own machine learning projects from problem definition through implementation
- Design and implement evaluation methodologies for AI and machine learning systems
- Create datasets, benchmarks, and metrics to measure model and product performance
- Evaluate and improve LLM-based systems, including RAG applications, agents, safety systems, and end-to-end AI products
- Analyze model behavior, identify failure modes, and recommend practical improvements
- Build and maintain ML pipelines, tooling, and evaluation infrastructure
- Collaborate with product, engineering, and research teams to translate business goals into measurable ML objectives
- Prototype and iterate rapidly to solve business and product challenges
- Communicate findings, trade-offs, and recommendations to both technical and non-technical stakeholders
Skills
- • 5+ years of experience in Machine Learning Engineering or a related field
- • Strong understanding of machine learning fundamentals and model evaluation
- • Strong Python programming skills and experience with modern ML frameworks such as PyTorch, TensorFlow, or JAX
- • Experience training, fine-tuning, or adapting machine learning models
- • Experience working with Large Language Models beyond simple API integration
- • Experience evaluating AI systems and translating results into actionable recommendations
- • Experience building and maintaining machine learning systems and pipelines
- • Understanding of retrieval-augmented generation (RAG), agentic systems, and LLM safety concepts
- • Ability to work effectively in ambiguous problem spaces with incomplete requirements and limited data
- • Strong written and verbal communication skills
- • Bachelor's degree in Computer Science or equivalent is required
- • Experience designing benchmarks, evaluation frameworks, or automated evaluation systems
- • Experience with distributed training or large-scale model inference
- • Experience building reusable ML tooling and internal platforms
- • Experience with cloud platforms and modern MLOps practices
- • Experience working on user-facing AI products at scale
- • Research experience or publications in machine learning or AI-related fields
Benefits
- Flexible schedule
- Benefits package - medical insurance, vision, dental, etc.
- Corporate social events
- Professional development opportunities
- Well-equipped office
Company Overview
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