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About the Role
Turn difficult problems in real workflows into research questions, develop improvements across models and agent systems, and carry results into production. This role is for a researcher with strong engineering ability who can move between model training, experimentation, and applied AI systems, choosing the right approach for our users.
Responsibilities
- Own applied research projects from problem definition and experiment design through implementation and production validation.
- Train and adapt models for reasoning, retrieval, and decision-making tasks.
- Develop and test improvements to agent systems, including tool use, context, memory, planning, and recovery from failures.
- Build datasets, evaluation environments, and grading methods that capture the difficulty of real workflows.
- Inspect model outputs and agent traces, identify recurring failure modes, and turn those findings into changes to training, data, or system design.
- Build the experimentation tools and pipelines needed to run, compare, and reproduce research efficiently.
- Partner with AI Strategy, product, and engineering to understand workflows and ship improvements to production.
Requirements
- Deep ML foundations: Understand learning, optimization, probability, and statistics, and use that understanding to reason about model behavior and experimental results.
- Hands-on model experience: Trained, adapted, or improved models and can explain the decisions behind your work.
- Research judgment: Turn ambiguous problems into clear hypotheses, design experiments, and develop new approaches.
- Strong applied judgment: Stay close to real workflows and know when to improve the model, the data, or the system around it. Weigh quality, reliability, latency, and cost.
- High agency and ownership: Help define the research direction, take responsibility for outcomes, and move from investigation to execution without waiting for a detailed plan.
- Clear collaborators: Communicate findings and uncertainty clearly, seek context from domain experts, and help research, engineering, and product make decisions.
Skills
- Model training
- Experimentation
- Applied AI systems
- Learning
- Optimization
- Probability
- Statistics
- Model adaptation
- Post-training
- Reinforcement learning
- Agent systems
- Tool use
- Context management
- Memory management
- Planning
- Failure recovery
- Dataset building
- Evaluation environments
- Grading methods
- Model output inspection
- Agent trace inspection
- System design
- Experimentation tools
- Experimentation pipelines
- Reward modeling
- Synthetic data
- Learning from human feedback
- Distributed training
- Inference optimization
- Large-scale experimentation
Benefits
- Competitive compensation, including meaningful equity.
- Medical, dental, and vision insurance for employees and dependents.
- Generous PTO policy.
- Paid parental leave.
About the Company
- At Valkai, we are committed to fostering a diverse and inclusive workplace.
Equal Opportunity
- We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.