[Hiring] Senior Director, Frontier AI Research @EPAM Systems

Role Description

The Senior Director, Frontier AI Research is EPAM's first dedicated Frontier AI research hire, operating across a broad set of capabilities rather than a single research domain. The role sits at the intersection of research, solution architecture, and technical sales, remaining technically engaged through early pilots while delivery leadership owns day-to-day execution.

The right person is a genuine technical peer to sophisticated AI researchers: someone who can translate ambiguous research goals into concrete specifications that delivery teams can execute at quality and scale.

Responsibilities

• Engage directly with researchers, engineers, and technical leaders at frontier AI labs to understand model training, post-training, and evaluation objectives.
• Translate ambiguous research goals into explicit target capabilities, failure modes, and success criteria, advising customers and internal teams on the right technical approach.
• Design rigorous evaluation approaches, including task taxonomies, benchmarks, rubrics, and graders, and analyze model outputs to identify failure modes and improvement opportunities.
• Help architect RL environments, reward functions, and verifiers that provide reliable training and evaluation signals while mitigating reward hacking and weak verification.
• Design technically rigorous training data programs (SFT, preference data, RLHF), defining task distributions, annotation criteria, and quality standards.
• Translate recurring customer research needs into repeatable EPAM Frontier AI offerings, methodologies, and technical assets.
• Partner with the Platform and Operations Lead to determine the tooling, infrastructure, and specialist talent required to deliver new offerings.
• Serve as the primary research and technical SME supporting Frontier AI Sellers in strategic customer pursuits, from discovery through solution design.
• Translate customer requirements into technically compelling solution designs, proposals, and pilot plans that establish credibility with sophisticated AI research organizations.
• Convert research requirements into clear specifications, including scope, rubrics, and quality thresholds, that delivery teams can execute without losing the research intent.
• Provide technical oversight during early and strategically important engagements, reviewing outputs and model behavior to confirm the program is producing the intended results.
• Maintain strong familiarity with developments in frontier model training, post-training, RL, agentic systems, and evaluation, and apply that knowledge to EPAM's offerings.
• Build relationships across the Frontier AI research ecosystem and contribute to technically credible customer-facing content and thought leadership.

Qualifications

• Significant experience in machine learning, AI research, or research engineering, with demonstrated work on modern deep learning and large language models.
• Strong understanding of the model training and post-training lifecycle, including SFT, RLHF, reward modeling, and evaluation.
• Demonstrated depth in one or more Frontier AI focus areas (evaluations, RL environments, agentic systems, reward or verifier design, coding agents), with the breadth to operate across adjacent areas.
• Experience translating ambiguous research objectives into structured experiments, datasets, evaluations, or environments.
• Hands-on technical capability, including strong Python skills and experience with modern ML frameworks, LLM APIs, and evaluation tooling.
• Demonstrated ability to communicate complex technical concepts clearly to both technical and cross-functional audiences.
• Advanced degree in Computer Science, Machine Learning, AI, Statistics, or a related field, or equivalent demonstrated research experience.

Requirements

• Peer-reviewed AI/ML research or a strong record of technically substantive applied research.
• Prior experience building solutions or conducting research for frontier AI labs or leading foundation model companies.
• Direct experience with RLHF, RLAIF, preference optimization, or synthetic data generation and curation.
• Comfortable operating as a genuine technical peer to sophisticated researchers, with the commercial awareness to translate research credibility into customer trust.
• Entrepreneurial mindset, comfortable building a new capability and creating structure where established process does not yet exist.