Tessera Labs
Research Scientist
San Jose Office (HQ)
Sponsorship not specifiedDetected 62 days ago
SnowflakeMachine LearningLLMsAgentic AIStatisticsBrand StrategySalesforceSAPHRISResearchMentoring
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odds of building a lasting career here
37Risky
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
- We're looking for a Research Scientist to set and pursue a research agenda for reliable long-horizon agents operating inside real enterprises.
- Almost nobody has the landscapes, the traces, or the customers to study it.
- Two make this an unusually good research setting.
Responsibilities
- Invent and validate methods for post-training agents on transformation work: reward design where verification is partial, delayed, or contested
- Own the question of what to measure. Develop evaluation methodology whose scores predict customer-observed correctness, and demonstrate where cheap automated proxies quietly fail.
- Study how multi-agent systems fail - error compounding across long trajectories, planning under partial observability of a landscape, delegation and verification between agents - and design against it.
- Turning process, data, and code into an ontology or knowledge graph an agent can reason over reliably - and one that stays true as the underlying systems change - is a research problem we own rather than inherit.
- Raise the team's research bar: review experiment designs, mentor engineers moving into research, and be the person who asks whether the result is real.
- Invent and validate methods for post-training agents on transformation work: reward design where verification is partial, delayed, or contested; RL formulations for long-horizon planning and tool use; curriculum and data strategy.
- Developing an evaluation methodology whose scores track expert-reviewed correctness on changes no automated test can verify, and showing where the cheap proxies disagree.
- Have exceptional research judgment - you pick the question well and kill your own ideas quickly when the evidence says to.
Company info
- Designing a reward formulation for transformation work that doesn't collapse into reward hacking when the agent discovers it can pass the regression suite by removing the branch the tests don't reach.
- Characterizing error compounding across a forty-step transformation plan and proposing a verification scheme that measurably arrests it.
- Designing a memory architecture for multi-day agent runs and showing, with evidence rather than anecdote, that it beats stuffing the context window.
- Showing that grounding an agent in a learned ontology of a customer's landscape beats retrieval over raw artifacts - or finding that it doesn't, and saving us a year.
- Running a post-training study across three or four open-weight model sizes and publishing what it says about where domain data quality beats parameter count.
- Releasing an enterprise agent environment and task suite as an open benchmark, and being honest in the paper about where it doesn't transfer.
- Have an MS or PhD in CS, ML, statistics, math, physics, or a related field - or research experience of comparable depth without the credential.
- Are hands-on. You write the code, run the experiments, and read the logs. This is not a role that directs research from a distance.
- Hold rigor and urgency at once. We need results that are true and results that arrive.
- Write clearly, and enjoy explaining a technical argument to people who aren't researchers.
- Experience owning a research direction end to end at a frontier lab or a strong academic group.
- Published work on agents, tool use, RL for LLMs, code generation or repair, reasoning, or evaluation.
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