Senior Machine Learning Engineer at Rubrik · Lumi · Lumi

Responsibilities

Owning the full training lifecycle for the SLMs and classifiers in SAGE s real-time enforcement path, including base-model selection, supervised fine-tuning, preference optimization (DPO/RLAIF), and distillation from frontier teacher models.
Training anomaly and action-severity models that catch novel agent-side attack patterns at real-time decision latency, such as supply-chain compromises or emergent destructive behaviors not covered by any explicit policy. Severity scores route the highest-impact events to Agent Rewind for precise remediation.
Designing adversarial training pipelines like purpose-built adversarial agents and automated red-teams whose outputs feed directly into the next training run, turning every discovered weakness into a permanent model improvement.
Pushing the pareto frontier of accuracy, latency, and cost for governance-specific tasks through deliberate post-training choices (LoRA, quantization-aware training, distillation recipes, GRPO, etc.) and validating the wins on production traffic patterns.
Designing multi-stage inference pipelines that handle both real-time enforcement (inline prompt, response, and tool-call blocking) and high-throughput batch workloads (offline scoring, back-testing, corpus mining) while processing billions of tokens daily across Global 2000 customer agent fleets.
Optimizing live deployments through shared GPU pools, KV-cache-aware routing, continuous batching, FP8/INT8 quantization, and speculative decoding to minimize inference cost while holding sub-second P99 SLOs.
Building serving-layer infrastructure that lets SAGE block agent prompts, responses, and tool calls in real time without becoming a latency bottleneck. This includes model gateway design, request routing, and graceful degradation.
Owning canary, shadow, and A/B traffic patterns so new model variants are validated against live customer traffic before they take enforcement decisions.
Designing automated data curation pipelines that mine live customer environments (with privacy and tenancy guarantees) for high-value per tenant training examples, such as long-tail violations, near-miss policy edges, or novel agent behaviors, and routing them back into the training loop for each customer.
Building automated policy back-testing by replaying historical agent traffic against new model and policy versions to catch regressions and recommend policy improvements before customer-visible deployment.
Building online evaluation systems for live model decisions, including shadow scoring, drift detection, calibration monitoring, and policy-coverage gap analysis, ensuring quality regressions surface in minutes rather than weeks.
Generating synthetic data using frontier teachers (adversarial prompts, policy-edge cases, multi-turn interactions) with evaluation that confirms synthetic data improves downstream quality, not just dataset size.

Qualification

Partnering with Product ManagementCommunicating model behaviorCollaborating with Agent Cloud platformHands-on experience trainingComfort operating at production scale$188Rubrik (RBRK)Inclusion @ RubrikAt Rubrik

Required

Partnering with Product Management, customer-facing teams, and security analysts to translate customer agent-governance requirements into well-scoped modeling problems, and pushing back when ML is the wrong tool.
Communicating model behavior, tradeoffs, and limitations clearly to non-ML stakeholders, such as product managers and enterprise security leaders, so model decisions are made with full context.
Collaborating with Agent Cloud platform, security engineering, and AI research teams to integrate new SLMs into the real-time enforcement path with the right latency, observability, rollback, and tenancy guarantees.
2+ years of professional ML experience with demonstrable end-to-end production ownership; you have taken models from training to serving real customer traffic and stayed accountable for them through post-launch iteration.
Proficiency in Python and PyTorch (or equivalent) for production-grade training and evaluation.
Hands-on experience training, fine-tuning, or distilling language models or classifiers in a production setting, including SFT and at least one preference-optimization technique (DPO, RLAIF, or RLHF).
Production experience with serving frameworks (vLLM, SGLang, TensorRT-LLM, or equivalent), including optimization involving continuous batching, KV-cache strategy, and inference-time quantization.
Experience designing closed-loop ML systems, including the eval, telemetry, data-curation, and synthetic-data infrastructure that turns production signals back into training data and the next model release. You have built (not just used) at least one such loop.
Comfort operating at production scale, including debugging models that handle high QPS in safety-critical request paths where errors have customer-visible consequences.
Deep background in AI safety and red-teaming, including hands-on experience with adversarial ML, prompt injection defense strategies, and automated evaluation suites for enterprise-grade LLM safety.
Expertise in model evaluation methodology, specifically building "LLM-as-judge" pipelines, calibration monitoring, and adversarial benchmarks that surface the subtle failure modes static metrics often overlook.
Experience with context-fusion and retrieval systems that synthesize disparate signals - such as data sensitivity, user identity, and behavioral history - into high-fidelity model decisions.

Preferred

The minimum and maximum base salaries for this role are posted below; additionally, the role is eligible for bonus potential, equity and benefits. The range displayed reflects the minimum and maximum target for new hire salaries for the role based on U.S. location. Within the range, the salary offered will be determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
$188,500 $282,700 USD
Join Us in Securing and Accelerating the World s AI Transformation
Rubrik (RBRK), the Security and AI Operations Company, leads at the intersection of data protection, cyber resilience, and enterprise AI acceleration. Rubrik Security Cloud delivers complete cyber resilience by securing, monitoring, and recovering data, identities, and workloads across clouds. Rubrik Agent Cloud accelerates trusted AI agent deployments at scale by monitoring and auditing agentic actions, enforcing real-time guardrails, fine-tuning for accuracy and undoing agentic mistakes.
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Inclusion @ Rubrik
At Rubrik, we are dedicated to fostering a culture where people from all backgrounds are valued, feel they belong, and believe they can succeed. Our commitment to inclusion is at the heart of our mission to secure the world’s data.

Senior Machine Learning Engineer

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Rubrik

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