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Research Engineer

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Research Engineer in USA

  • Tessera Labs
  • Full time
  • Email
  • San Jose, CA

Responsibilities

  • Build and scale post-training infrastructure for SFT, preference optimization, and reinforcement learning involving long-horizon tool use and enterprise transformations.
  • Develop memory, context, ontology, and knowledge-graph systems for long-running agents.
  • Create data-generation and curation pipelines for synthetic landscapes, transformation traces, tool-call trajectories, and curriculum infrastructure.
  • Build sandboxed RL environments and execution-and-verification harnesses with automatically scored outcomes.
  • Own offline evaluation infrastructure for trajectory-level agent behavior, reproducible task suites, and long-term result tracking.
  • Run experiments end to end, including design, execution, debugging, analysis, and interpretation.
  • Optimize distributed training and inference throughput through kernels, parallelism, memory, batching, serving, and long-context techniques.
  • Take trained models into production serving through quantization, serving configuration, and rollback planning.
  • Establish standards for experiment reproducibility, tracking, and result hygiene.

Requirements

  • Significant experience training, fine-tuning, or post-training language models with demonstrated results owned by the candidate.
  • RL experience such as RLHF, RLAIF, RLVR, GRPO-family methods, or agentic RL is close to a requirement.
  • Experience or strong understanding of memory and context for long-running agents through architecture, retrieval, or training.
  • Strong software engineering fundamentals and the ability to write experiment code that others can run.
  • Fluency in Python and PyTorch or JAX, with the ability to debug distributed training.
  • Experience with GPU infrastructure at scale and understanding of training time and memory usage.
  • Ability to design, run, and interpret rigorous experiments and distinguish effects, noise, and bugs.
  • Clear written communication and interest in taking research results into production.
  • Preferred experience includes RL environments, execution sandboxes, verifiable-reward task suites, long-context modeling, knowledge graphs, ontologies, semantic layers, production agent memory, code models, or open-source ML systems.
  • Preferred evidence includes contributions to systems such as vLLM, SGLang, PyTorch, Triton, DeepSpeed, Ray, Megatron, or TRL; publications, technical reports, or open-source releases; and an advanced degree in a quantitative field or equivalent industry research experience.

Salary: $200k - $300k/yr

Tessera Labs

Enterprise transformations shouldn't take years or cost fortunes. At Tessera, we've built a multi-agent AI platform that cuts ERP transformation timelines from years to weeks and reduces costs by more than half—while delivering first-time-right outcomes with enterprise-grade security and governan...

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