Applied Research

Location: San Francisco, New York, London, or remote

Cash compensation: $150,000–$300,000 per year plus equity incentives

Company

hirify.global is building an open superintelligence stack for AI teams, combining compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for frontier-scale post-training.

What you will do

• Build and post-train agents for scientific research, including literature search, scientific database queries, code execution, domain-tool use, and experiment selection under fixed compute or experimental budgets.
• Turn research partners’ workflows, datasets, and outcome criteria into trainable tasks, evaluations, and reward signals.
• Design verifiers for correctness, novelty, and reproducibility, including checks for label leakage, lookup shortcuts, unsupported conclusions, and evidence use.
• Connect programmatic checks, simulations, domain models, and expert feedback to training while handling noisy or delayed results, calibrating confidence, and defining when human review is needed.
• Develop and evaluate RL and multi-agent methods for proposing, testing, and critiquing hypotheses against strong single-agent baselines at equal compute.
• Maintain reproducible code, data, logs, and experiment configurations; rerun results, measure noise, perform ablations, and release reusable environments, evaluations, and research code.

Requirements

• Depth in reinforcement learning, agents, or post-training systems, with the ability to acquire relevant scientific context.
• Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops.
• Strong experimental judgment, including meaningful baselines, careful trace inspection, clear failure attribution, and willingness to change direction when evidence changes.
• Strong Python skills and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.
• Comfort owning ambiguous problems and keeping research partners and teammates current while resolving them.

Nice to have

• Prior work in autonomous research, machine learning for science, scientific computing, or reproducible computational research.
• Publications, open-source systems, benchmarks, or shipped products demonstrating exceptional work, including difficult projects, failures, and evidence-driven changes in approach.

Culture & Benefits

• Externally facing applied research role with direct work with customers, users, or research partners alongside a substantive research agenda.
• Flexible work from San Francisco, New York, London, or remotely.
• Visa sponsorship and relocation support.
• Professional development budget.
• Team off-sites and conference attendance.