
Lexsi Labs is dedicated to building the foundations for Safe Superintelligence by integrating alignment theory, interpretability science, and agentic autonomy into a cohesive research framework.…

Lexsi Labs is dedicated to building the foundations for Safe Superintelligence by integrating alignment theory, interpretability science, and agentic autonomy into a cohesive research framework.…
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Lexsi Labs is a frontier AI lab building aligned, interpretable, and safe superintelligent systems. Our work spans alignment methods, interpretability-led system design, and foundational model research across LLMs, agents, and tabular / structured-data models.
A core part of our work is turning advanced AI research into production systems. That means taking internally developed libraries and model workflows, such as AlignTun e, TabTun e, and DLBacktrac e, and integrating them into a scalable platform infrastructure that supports training, inference, evaluation, observability, and enterprise deployment
.
We are hiring a Senior AI Platform Engine er to build the platform layer that operationalizes Lexsi’s core AI system
s.
This role is centered on R&D-to-platform roll out: taking technically sophisticated model systems and making them usable, reliable, and scalable inside the product stack. You will work across model pipelines, training systems, inference infrastructure, distributed execution, and backend platform architectu
re.
This is not a thin integration role. It requires strong engineering depth and enough model understanding to work effectively with systems involv ing fine-tuning, RL, alignment, interpretability, agent execution, and inference optimiza tion acr oss LLMs, agents, and tabular foundation mo d
**els.
Responsibil**
**nable.
Example Problems You Might Wo**
**ructure.
Requi**
**ional cost
Strong Bo**
nus Signals
Expe rience with alignment, interpretability, or AI sa
fety systemsExpe rience with multi-cluste r scheduling, inference optimization, or serving infrastructure for
large modelsExperience converting internal research frameworks into reusable platform
**odel systems
What This**
Role Is Not:
Not a prompt-en
gineering roleNot a glue-code in
tegration roleNot a research-only role disconnected f
rom deploymentNot
a junior roleThis role is for engineers who want to work on the hardest layer in applied AI: the b oundary where model science, platform architecture, and prod uction sy
stems collide.
ities
Own the platformization of Lexsi’s internal AI libraries, turning research-heavy systems into robust platform capabilities with stable APIs, execution layers, observability, and deployment
paths.Build and scale training and post-training infrastructure for workflows, incl uding SFT, RL, evaluation, model adaptation, and agent optimi z
ation.Design the integration layer between research systems and product infrastructure, including job orchestration, artifact management, dataset versioning, experiment lineage, and runtime control sur
faces.Build inference systems that can support complex model behaviors under production constraints, including latency, throughput, cost efficiency, debuggability, and s
afety.Desig n for multi-cluster and distributed exe cution, including scheduling, fault tolerance, checkpointing, retries, workload isolation, and heterogeneous compute environ
ments.Operationalize systems su ch as Ali gnTune for fine-tuning and RL pipel ines, T abTune for tabular foundation model workflows , and DLBac ktrace for interpretability, tracing, and behavioral inspe
ction.Build common platform primitives for model lifecycle management across training, evaluation, serving, rollback, and monit
oring.Partner closely with research teams to translate model-science complexity into production architecture without flattening away the core technical
value.Improve platform reliability for long-running and failure-prone AI workloads, especially where model behavior, system behavior, and infrastructure behavior interact in non-trivial
ways.Ensure that alignment, interpretability, and auditability are embedded into system design, especially for enterprise and regulated deployments where model outputs and decisions must be explai
rk On **:
Turn A** lignTune into a production-grade internal service for supervised fine-tuning and reinforcement learning across multiple model families, datasets, and evaluatio
n loops.Build rollout infrastructure for new model-science capabilities so research systems can be exposed safely and incrementally inside the p
latform.In tegrate DLB acktrace into training and inference pipelines so model behavior can be traced, debugged, and surfaced through internal and external product s
urfaces.Build inference architecture for large models and agent systems that must balance cost, performance, explainability, and runtime
control.Design distributed execution flows across clusters for long-running training, evaluation, and analysis workloads with strong guarantees around recovery and reproduc
ibility.Unify workflows across LLMs, agents, and tabula r models without collapsing their distinct operational and scientific requirements into a one-size-fits-none abst
raction.Build the platform interfaces that let downstream teams launch, inspect, evaluate, and deploy complex model workflows without needing to reimplement research infrast
rements:
Strong experience building and shipping complex AI / ML systems in
productionDeep backend and platform engineering experience, espe cially in Python, distributed services, workflow orchestration, data systems, and cloud infr
astructureHands-on experience with one or more of: fine-tuning systems, RL pipelines, inference infrastructure, distributed training, model serving, evaluati
on systemsStrong understanding of the systems implications of modern model workflo ws across LLMs, agents, and structured / tabular mod
el systemsExperience scaling workloads across clusters and production environments, with strong instincts around reliability, observability, and p
erformanceAbility to work across research code, systems code, and product infrastructure without losing rigor at ei
ther layerStrong technical judgment around the tradeoffs between model quality, infra complexity, scalability, interpretability, and operat
capabilitiesExperience debugging production failures caused by interactions between model behavior, orchestration systems, and in
frastructureExperience with agent runtimes, tool orchestration, long-horizon execution, or stateful m