Technical Project ManagerGrid Dynamics

We are looking for an Technical Project Manager to lead complex, cross-functional programs at the intersection of AI/ML research, synthetic data generation, evaluation, and developer tooling. This person will partner closely with research scientists, ML engineers, platform/tooling teams, and downstream consumers to turn exploratory research into scalable capabilities and repeatable workflows.
The ideal candidate combines strong technical depth with excellent program leadership. They should be comfortable operating in areas where requirements are initially ambiguous, helping research teams structure experimentation while simultaneously driving the infrastructure, tooling, interfaces, and operational processes required to scale successful approaches across multiple teams.

Essential functions

• Lead the end-to-end synthetic data generation program across research, engineering, tooling/platform, evaluation, and downstream model teams.

• Partner with researchers to understand research objectives, experimental approaches, hypotheses, dependencies, and criteria for determining when an approach is ready to scale.

• Translate research concepts and prototypes into engineering requirements, platform capabilities, tooling needs, and executable roadmaps.

• Define and manage the synthetic-data lifecycle, including data generation, filtering, transformation, labeling, quality validation, evaluation, versioning, storage, distribution, and consumption.

• Drive development of reusable tools and platforms rather than one-off pipelines, identifying common capabilities that can serve multiple research and modeling teams.

• Coordinate dependencies across teams responsible for models, data infrastructure, evaluation, compute, tooling, and downstream product/model integration.

• Establish mechanisms for data provenance, reproducibility, dataset/version tracking, quality metrics, experiment tracking, and governance.

• Partner with researchers and engineers to define success metrics for synthetic datasets, including quality, diversity, coverage, correctness, contamination, and impact on downstream model performance.

• Build program structures that support the inherently iterative nature of research while still providing clear visibility into milestones, risks, decisions, dependencies, and resource requirements.

• Facilitate technical tradeoff discussions across research and engineering teams and drive decisions when teams have competing requirements or priorities.

• Identify opportunities to automate manual research workflows and improve researcher productivity through better tooling.

• Communicate complex technical programs clearly to engineering, research, and leadership audiences.

Qualifications

• 7+ years of technical program management, engineering program management, product/program management, or equivalent experience working with software engineering organizations.

• Demonstrated experience leading large, technically complex programs spanning multiple engineering or research teams.

• Experience working directly with ML researchers, research scientists, ML engineers, or data scientists.

• Strong understanding of modern ML development workflows, including training data, model training, experimentation, evaluation, and iteration.

• Experience delivering developer tools, ML tooling, data platforms, internal platforms, or infrastructure products.

• Ability to understand system architecture and engage engineers on APIs, data pipelines, storage, compute, orchestration, dependencies, and scalability.

• Demonstrated ability to translate ambiguous research or technical problems into structured programs without imposing artificial certainty on exploratory work.

• Strong technical communication skills and the ability to explain architecture, dependencies, tradeoffs, and technical risks to different audiences.

• Proven ability to drive alignment across teams that do not report into the same organization.

• Advanced degree (MS/MBA) in a technical, organizational, or communication-related field.

Would be a plus

• Experience with synthetic data generation, data augmentation, simulation, or AI-generated training/evaluation datasets.

• Understanding of LLM or foundation-model development, including pre-training, fine-tuning/post-training, evaluation, and data curation.

• Experience with data-generation pipelines using LLMs, including prompt/template management, sampling strategies, filtering, deduplication, quality scoring, and evaluation.

• Experience with human evaluation and/or automated evaluation systems, including model-based graders.

• Familiarity with dataset lineage, provenance, versioning, reproducibility, and data governance.

• Experience building or managing self-service tooling for researchers and ML engineers.

• Familiarity with large-scale distributed data processing and ML infrastructure.

• Working knowledge of Python, SQL, APIs, or similar technologies—not necessarily as a production engineer, but enough to inspect workflows and have substantive technical conversations.

• Experience taking capabilities from research prototype → productionized tool/platform → broad organizational adoption.

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 onboarding 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.