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About the Role
This role focuses on building and scaling the AI models that power Tessera's transformation engine. You will own the research and engineering of frontier models, turning hypotheses into production-ready solutions. The work involves developing training, environment, evaluation, and inference machinery, with a strong emphasis on Reinforcement Learning (RL) and verifiable task spaces.
Responsibilities
- Build and scale the post-training stack, including SFT, preference optimization, and RL for long-horizon tool use.
- Develop memory and context machinery for long-horizon agents.
- Construct the representation layer for agent reasoning, including ontologies and knowledge graphs.
- Design and implement data generation and curation pipelines for training models on enterprise systems.
- Build RL environments with sandboxed landscapes and execution-and-verification harnesses.
- Build and run the offline evaluation harness for long-horizon agentic behavior.
- Run experiments end-to-end, from design to analysis.
- Optimize training and inference throughput, focusing on kernels, parallelism, memory, and batching.
- Manage the deployment of training results into production, including quantization and serving configuration.
- Establish standards for reproducibility, experiment tracking, and result hygiene.
Requirements
- Significant experience training, fine-tuning, or post-training language models with demonstrable results.
- RL tuning experience (RLHF, RLAIF, RLVR, GRPO-family, or agentic RL) is nearly a requirement.
- Experience with memory and context for long-running agents.
- Strong software engineering fundamentals, producing runnable code for experiments.
- Fluency in Python and PyTorch (or JAX).
- Comfort debugging distributed training.
- Ability to design, run, and interpret experiments with empirical rigor.
- Experience with GPU infrastructure at scale, understanding time and memory usage.
- Desire for research to end up in production.
- Clear written communication skills.
Skills
- Post-training
- SFT
- Preference optimization
- Reinforcement Learning (RL)
- Long-horizon tool use
- Transformation
- Reconciliation
- Enterprise systems
- Memory systems
- Context machinery
- Agent behavior
- Representation layer
- Ontologies
- Knowledge graphs
- Data generation pipelines
- Data curation pipelines
- Synthetic landscapes
- Transformation traces
- Tool-call trajectories
- Curriculum infrastructure
- RL environments
- Sandboxed landscapes
- Execution-and-verification harnesses
- Offline evaluation
- Reproducibility
- Experiment tracking
- Result hygiene
- Training optimization
- Inference optimization
- Python
- PyTorch
- JAX
- Distributed training
- GPU infrastructure
Location
- Remote
Work Type
- Full-time
Experience Level
- Mid-level
- Senior
Education Level
- Advanced degree in CS, ML, math, physics, or related quantitative field, or equivalent industry research experience.
About the Company
- Tessera Labs is an enterprise software company developing an AI platform to transform how large companies operate.
- The platform, Tessera, acts as a transformation engine, understanding and modifying enterprise processes, data, and code within weeks.
- It is designed to be vendor-agnostic, working with systems like SAP, Salesforce, Workday, Oracle, Snowflake, and MuleSoft.
- Key features include robust governance with traceable and reversible actions, and generality to handle unique enterprise complexities.
- Tessera Labs sells a product, not a service, focusing on making the product successful.
- The company has raised a $60M Series A led by Andreessen Horowitz, with participation from Foundation Capital, Myriad Venture Partners, and Osage University Partners.
Equal Opportunity
- No third party may recruit, solicit candidates, publish job opportunities, use Tessera Labs’ name or branding, or represent that they are acting on behalf of Tessera Labs without prior written authorization. Any such activity conducted without explicit written consent is strictly prohibited.