Data Manager with AI

Lead end-to-end AI/ML and data programs, coordinating Data Engineering, Data Science, ML Engineering, Product, and Infrastructure teams to deliver large-scale AI initiatives.

Define and manage data strategy, requirements, pipelines, data quality, governance, metadata, and lineage to support AI/ML model development and production workloads.

Drive programs involving Generative AI, LLMs, Agentic AI, model training/evaluation, and AI data platforms, ensuring milestones, dependencies, and deliverables are aligned.

Establish data quality and program KPIs, identify risks and bottlenecks, and work with engineering teams to resolve issues related to data availability, accuracy, scalability, and model readiness.

Prepare executive-level status updates, roadmaps, and risk assessments while managing cross-functional stakeholders and ensuring AI data programs are delivered on time and within scope.

Role Type: Contractor (flexible, Part-Time or Full-Time)

Location: Remote

micro1 is engaging Principal Research Scientist (Computer Science / Computational Physics)s to contribute to an advanced customer project at the intersection of computational theory and AI. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters. You will work with frontier AI models and agents to tackle open problems, drive research direction, and define standards for scientific breakthroughs. Your insights and rigorous approach will be pivotal in charting what counts as genuine progress within your subfield.

Scope of Work

Identify, frame, and articulate foundational open problems, theoretical bounds, or simulation challenges where verifiable progress can be demonstrated.
Guide AI models and agents in multi-step research processes—ranging from proofs and algorithms to numerical experiments and high-performance simulations.
Critically assess and validate results, utilizing proof checks, computational reproduction, and formal methods to pinpoint the limits of model-generated reasoning.
Evaluate and document the novelty, correctness, and significance of research outputs relative to current literature and established results.
Produce clear, concise research memos detailing research attempts, partial solutions, and observed model limitations or failure patterns.
Advise on research directions, recommending promising subfields and open problems suitable for advancement via AI models and computational tools.

Preferred Qualifications

7 - 10+ years of active research in computer science or mathematical/computational physics, with a strong record of scholarly contributions.
Recognition at the field level—such as major prizes, society fellowships, or plenary/invited talks at top scholarly events.
A consistent publication record in leading venues within your domain, with recent results published in the past year.
Demonstrated success resolving named open problems or introducing influential concepts adopted by peers.
Deep, hands-on experience with computational research methods: simulation, numerical analysis, algorithmic implementation, and/or high-performance computing environments.
Proven ability to rigorously examine technical work outside your immediate area of expertise.
Experience on program committees, editorial boards, or with formal research tools (e.g., Lean, Coq, SAT/SMT solvers) is a strong plus, as is a public record of outlining open problems or showcasing research with advanced AI systems.