Tessera Labs
Research Engineer
San Jose Office (HQ)
Sponsorship not specifiedDetected 62 days ago
PythonSnowflakeMachine LearningPyTorchBrand StrategySalesforceSAPHRISResearch
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odds of building a lasting career here
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Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- Frontier models have never seen most of what we work on.
- Proprietary dialects, customer-specific configuration, semantics that exist in no public corpus, and tasks that run forty steps before anything tells you whether you were right.
- Closing that gap with post-training, environments, and evaluation is a research problem, and Research Engineers are the people who make it happen at scale.
Responsibilities
- Build the memory and context machinery that long-horizon agents run on: what an agent retains across a forty-step run, how it's structured, retrieved, compacted, and revised - and how you train a model to use it well rather than bolting it on at inference.
- Build the representation layer that agents reason over - ontologies and knowledge graphs derived from real enterprise systems - and the pipelines that construct, validate, and keep them current.
- Design and implement data generation and curation pipelines - synthetic landscapes, transformation traces, tool-call trajectories, curriculum infrastructure - that teach models to operate systems no public model has seen.
- Build the RL environments: sandboxed landscapes and execution-and-verification harnesses where a change can be applied, checked, and scored automatically, backed by design-partner traces where synthetic data won't do.
- Build and run the offline eval harness for long-horizon agentic behavior - trajectory-level scoring, task suites, and the infrastructure that makes a result reproducible six weeks later. You own the harness
- the methodology it implements is a shared argument with Research Science.
- Run experiments end to end - design, launch, debug, analyze - and be honest about which effects are real and which are noise.
- Optimize training and inference throughput: kernels, parallelism strategies, memory, batching, serving. Long context matters here more than most places
- Establish standards for reproducibility, experiment tracking, and result hygiene, so findings survive contact with the next person who builds on them.
- Building a synthetic landscape generator that produces enterprise systems with realistic complexity and coupling, then measuring what training on it actually buys on held-out real customer environments.
Requirements
- A track record of publications, technical reports, or open-source releases - we treat these as equivalent currency.
- An advanced degree in CS, ML, math, physics, or a related quantitative field, or equivalent industry research experience.
Benefits
- Build and scale the post-training stack: SFT, preference optimization, and reinforcement learning for long-horizon tool use, transformation, and reconciliation over enterprise systems.
Company info
- Governance: every action is logged, traceable, and reversible, because our customers are regulated and these are the systems that close their books.
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