Senior Director, Frontier AI Research, Bay Area, NY

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Senior Director, Frontier AI Research, Bay Area, NY

Remote in United States of America: San Francisco, United States of America: New York

Business Development

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EPAMs new Frontier AI business unit partners directly with leading AI labs and advanced AI organizations, translating their research and post-training objectives into technically rigorous, deliverable programs across evaluations, RL environments, and training data.

The Senior Director, Frontier AI Research is EPAMs 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.

Req.#1072890310

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 EPAMs offerings

• Build relationships across the Frontier AI research ecosystem and contribute to technically credible customer facing content and thought leadership

Requirements

• 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

Nice to have

• 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