Website:
lexsi.ai
Job details:
Lexsi Labs is the leading frontier AI lab focused on building aligned, interpretable, and safe superintelligent systems. While that is the vision, the mission to build safety aware autonomous system in the extreme near term. Our research work spans around areas like AI alignment methodologies, interpretability led system design, and foundational model research across structured, tabular, and new autonomous system designs. We published about 25+ papers in the past 15 months across leading conferences ICLR, ICML, WWW, IJCNN, MICCAI, Eurips etc. Our labs are located in India (Mumbai & remote), Paris, & London.
We operate with a flat structure, high autonomy, and a strong bias toward engineers who take full ownership of what they build, from architecture to production behavior.
The RoleMost of the variance in agent performance comes from the system around the model, not the model itself. This role researches that system. The coding agent is the primary testbed: it takes an objective, works on a repository, verifies its own changes, and produces a record of what it did. It runs inside a customer's network, usually with no internet egress, against codebases that are large, old, thinly tested and load-bearing. Small models under real latency and cost budgets are a target, not a fallback.
Three areas:
Harness. Action space and tool surface design, context construction policy, where deterministic program analysis should replace model inference, control topology (single loop versus decomposition), verification design, and allocation of inference-time compute.
Memory. Schemas for execution state held outside the context window, compaction policy and what it destroys, retrieval under closed-world constraints, and whether an agent measurably improves on a repository over time.
Evals. Task construction from real repository history with executable verification, scoring partially-checkable long-horizon work, variance and contamination control, and failure taxonomies that attribute a failure to a component.
The work is controlled experiments on architecture, not prompt tuning: isolate a failure from eval traces, form a hypothesis about the responsible component, change it, ablate it, and establish whether the gain holds across repositories and model sizes. Outputs are papers, benchmarks and released tooling where we can publish, and shipped architecture where we cannot.
You will work directly with our alignment and interpretability researchers on post-training, behavioral evaluation, and reading what an agent actually did rather than what its trace claims.
What We Are Looking ForResearch judgment with systems depth. You should be able to isolate a failure, design the experiment that tests your explanation of it, build what the experiment needs, and tell the difference between a real gain and an artifact of your setup.
Agentic systems. Two or more years building agents that ran against real workloads, not demos. Working knowledge of the current landscape (ReAct-style agents, LangGraph, LangChain, Semantic Kernel, the current generation of coding agents) and a specific account of where each stops working. Experience with tool-use protocols and orchestration under partial failure, retries, timeouts and non-idempotent actions.
Evaluation. You have built an eval dataset or harness that other people then used. Comfortable with SWE-bench-class benchmarks and their construction, containerised task execution at scale, statistical treatment of noisy multi-run results, and pass@k, best-of-N and majority-vote scoring and their failure cases.
Code intelligence and program analysis. ASTs and tree-sitter, static and dataflow analysis, symbol indexing and code search, call graph and dependency resolution, codemods and automated migration tooling. You should be able to build a repository-scale index and defend its design.
Backend and infrastructure. Advanced Python. Sandboxing and containerisation (Docker, gVisor, Firecracker or equivalent), distributed execution of thousands of parallel trials, artifact and dependency management, and on-premise or air-gapped deployment. Experiments that cannot be run at volume are not useful here.
Observability. Distributed tracing, structured logging, OpenTelemetry, and the replay and inspection tooling that makes a non-deterministic system debuggable end to end.
Model side. You do not need to be a training specialist, but you should be fluent in post-training methods (SFT, DPO, RL for agents), distillation, inference-time scaling, and quantisation and serving trade-offs at small parameter counts. You should have a view on where architecture ends and the model begins.
You treat performance, reliability, cost, safety and interpretability as one connected set of constraints, and you make reasonable calls when the problem is loosely specified and ownership is assumed rather than assigned.
Qualifications- Five or more years in research engineering, systems, or ML infrastructure, with at least two on agentic or LLM-based systems. A PhD in ML, PL, systems or a related field counts toward this, as does equivalent industrial research work.
- A public record we can read: first-author publications at ML or systems venues, a maintained open-source project, a benchmark, or a substantial technical writeup. We weigh a well-argued repository as heavily as a paper.
- Demonstrated ownership of a system from design through production behavior.
Useful but not required: developer tooling or IDE internals, language servers and LSP, refactoring engines, compiler work, large monorepo or legacy modernisation programs, RL infrastructure, and alignment, interpretability or safety tooling in production.
We publish. We move quickly and expect candidates to do the same. We value substance over polish and execution over rhetoric
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