Senior ML Engineer

Grid Dynamics; United States; On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Flexible schedule

Medical insurance

Vision and dental

Professional development

Well-equipped office

Job summary

Grid Dynamics is seeking a Machine Learning Engineer who can own projects from problem definition through production, design evaluation pipelines, and advance LLM-based systems including RAG and agents.

You will collaborate with product, engineering, and research teams to translate business goals into measurable ML objectives, iterate rapidly in ambiguous environments, and clearly communicate findings and trade-offs to both technical and non-technical stakeholders.

Qualifications

• 5+ years of experience in machine learning engineering or a related field.
• Strong Python and ML framework experience; knowledge of model evaluation.
• Experience with LLMs, RAG architectures, and AI safety concepts.
• Ability to translate business goals into measurable ML objectives.
• Experience building ML pipelines and evaluation infrastructure.

Responsibilities

• Own machine learning projects from problem definition through implementation.
• Design and implement evaluation methodologies for AI/ML systems.
• Create datasets, benchmarks, and metrics to measure model and product performance.
• Evaluate and improve LLM-based systems, including RAG applications and safety systems.
• Collaborate with product, engineering, and research teams to translate goals into ML objectives.
• Prototype and iterate rapidly to solve business challenges.
• Communicate findings and trade-offs to technical and non-technical stakeholders.

Skills

Python

ML fundamentals

Model evaluation

LLMs/RAG

ML pipelines

Communication

Education

Bachelor's degree in Computer Science

Tools

PyTorch

TensorFlow

JAX

Job description

We are looking for a Machine Learning Engineer with practical engineering skills and a deep understanding of modern AI systems, including LLMs, RAG architectures, agents, and safety considerations.Success in this role requires the ability to work in ambiguous environments, define measurable objectives, create evaluation methodologies when none exist, and rapidly iterate toward effective solutions.

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.

Requirements

• 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

Preferred Qualifications

• 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

We offer

• Opportunity to work on cutting-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, vision, dental, etc.
• Corporate social events
• Professional development opportunities
• Well-equipped office
• Please note that all onboardings must occur in person and you may be asked to travel to attend.

About Us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.