Senior Research Manager

FirstPrinciples

$120K — $150K *
US-AnywhereRemote in Canada
Enterprise Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • PhD or equivalent research experience in machine learning, computer science, physics, mathematics, or scientific computing.
  • Record of original research through publications or created systems.
  • Experience managing researchers and leading ambitious programs.
  • Deep expertise in at least one relevant area and ability to connect with adjacent disciplines.
  • Strong programming and experimental skills for deep engagement in implementation.
  • History of transforming research into validated and reusable capabilities.
  • Excellent judgment on novelty, evidence, and technical risk.

Responsibilities

  • Define a multi-faceted research agenda focusing on immediate capabilities and long-term innovations.
  • Mentor and develop a team of researchers with strong scientific insight and technical expertise.
  • Connect research outcomes to product and market needs while maximizing project potential.
  • Frame strategic research questions and design comprehensive experiments for evaluation.
  • Stay engaged with model development and critical implementation decisions throughout the process.
  • Create integrated teams that involve engineering and domain experts from the start of projects.
  • Facilitate the transition of prototypes into reliable and reusable systems.

Benefits

  • Opportunity to lead a high-impact research team.
  • Collaborative environment working with multidisciplinary experts.
  • Engagement in innovative and cutting-edge research programs.
  • Professional growth through mentorship and development of researchers.
  • Influence on research strategy and the future direction of the organization.
Full Job Description
The Role

We are seeking a Senior Research Manager and Research Lead to head a team of four to six researchers and define high-conviction research programs for Theo.

This is a technical research leadership role. You will remain close to the work-shaping hypotheses, experiments, model and data strategy, training methods, evaluations, and key implementation decisions-while developing exceptional researchers.

You will operate in a matrixed organization, bringing together researchers, engineers, physicists, mathematicians, and product specialists in multidisciplinary squads. Research and engineering begin together and remain jointly accountable as ideas move from exploration to validated capability, system integration, scientific use, and deployment.

What You'll Do
  • Define a research agenda spanning near-term capabilities, reusable platforms, and field-shaping bets.
  • Manage, mentor, and grow researchers with strong scientific taste, technical depth, and ownership.
  • Fully own the business impact of research by connecting it to product, market, and user needs, setting clear milestones and maximizing the potential for projects to turn into capabilities.
  • Frame bold research theses and design decisive experiments, baselines, evaluations, and failure analyses.
  • Remain technically engaged in model development, post-training, data strategy, evaluation, and critical implementations.
  • Form integrated squads with engineering and domain experts from the outset of a program.
  • Help prototypes become reliable, reproducible, and reusable systems without losing their scientific insight.
  • Build compounding assets such as models, datasets, verifiers, benchmarks, simulations, agent runtimes, and research infrastructure.
  • Use evidence to decide when to deepen, redirect, scale, publish, protect, open-source, deploy, or conclude a line of work.
  • Influence broader research strategy, hiring, technical standards, infrastructure, and resource allocation.
Research Questions You May Pursue
  • How can code execution, symbolic mathematics, theorem proving, and simulation provide scalable training and verification signals?
  • How can verifier-guided reinforcement learning and self-distillation improve long-horizon scientific reasoning?
  • How should scientific agents generate, test, revise, and preserve hypotheses across complex research programs?
  • What representations of mathematical functions and physical systems improve reasoning beyond text?
  • How can learned surrogate models and simulation-in-the-loop methods accelerate scientific discovery?
Who You Are:
  • A strong record of original research through publications, open-source systems, deployed methods, or comparable contributions.
  • Experience managing researchers or providing sustained technical leadership across ambitious programs.
  • A PhD or equivalent research experience in machine learning, computer science, physics, mathematics, scientific computing, or a related field.
  • Deep expertise in at least one relevant area, with the breadth to connect it to adjacent disciplines.
  • Strong programming and experimental skills, including the ability to engage deeply in implementation.
  • A record of moving research beyond demonstrations into validated, dependable, and reusable capabilities.
  • Excellent judgement about novelty, evidence, technical risk, and where to place long-term bets.
  • The ability to build trust and alignment across research, engineering, and scientific disciplines.
  • A low-ego, high-agency leadership style grounded in curiosity, precision, and intellectual honesty.

Relevant backgrounds may include scientific reasoning, reinforcement learning, post-training, agentic systems, automated conjecturing, theorem proving, world models, model merging, mechanistic interpretability, geometric deep learning, symbolic mathematics, AI for physics or mathematics, model architecture, scientific evaluation, physics-informed AI, differentiable simulation, or learned surrogate models.

Deep knowledge of physics or mathematics is especially valuable. We also welcome exceptional AI researchers motivated to develop that domain depth.

Application

Please submit a CV or resume and a brief statement describing:
  • A research direction that could materially advance AI-enabled scientific discovery.
  • A program you have led from initial hypothesis to evaluated results.
  • Your approach to technical leadership and multidisciplinary collaboration.

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