SDE II - AI Platform

Most boards and executives are currently flying blind when it comes to cyber risk. They are guessing. At Safe, we’ve built an AI-driven engine that finally gives the C-Suite a clear, quantified, and real-time view of their security posture. We don’t just provide data; we provide certainty.

We are a$170M Series C-funded category leader. We don’t play in the mid-market; we operate at the highest levels of global enterprise. Today, we are proud to serve 10% of the Fortune 500, protecting global icons such as Apple, Netflix, AT&T, Verizon, and Victoria’s Secret.

As we scale toward our next chapter, we are looking for high-performers who want to do the best work of their careers at the intersection of AI and Cybersecurity.

The Culture Memo: Our Operating System

Safe is not a typical corporate environment. We are a high-intensity, mission-driven team. We value builders who want to define a category and work alongside people who are equally committed to excellence.

• Extreme Ownership: We don’t do "not my job." We hire people who see a gap and own the solution from start to finish.

• The Elite Standard: We serve the most sophisticated companies on the planet. Our work must be bulletproof. Whether it’s a line of code or a sales deck, we aim for Tier-1 quality every time.

• Methodology & Rigor: We don’t wing it. FromForce Management andMEDDICC in sales todata-driven sprints in engineering, we rely on proven frameworks to stay disciplined and predictable.

• Radical Candor: We move too fast for politics or sugar-coating. We value direct, honest feedback that helps us find the right answer quickly.

• The Series C Hustle: We have the stability of a well-funded leader but the heart of a startup.

The Perks & Ownership:

We want our team to feel like owners because theyare owners. We trust our people to manage their results and their time.

• Meaningful Equity: Every "Safestar" is a shareholder. You aren’t just an employee; you are a partner in our success.

• Unlimited Leaves: We don’t believe in clock-watching. We offer unlimited leave because we trust you to take the time you need to recharge while staying committed to the mission.

• Comprehensive Benefits: We provide top-tier medical insurance and wellness benefits to ensure you and your family are well cared for.

• Career Trajectory: We are growing aggressively. For high-performers, the path for advancement moves at the speed of your ambition.

What You'll Do:

• Build the AI platform: APIs, SDKs, and self-service workflows so AI/ML engineers deploy, version, evaluate, and monitor workloads without touching raw infrastructure.

• Own GPU infrastructure: Cluster design, provisioning, scheduling, isolation, and utilisation optimisation across shared multi-team demand.

• Run inference in production: vLLM, Triton, KServe, or Ray — continuous batching, autoscaling, model routing, and multi-model serving against real latency and throughput SLOs.

• Optimize relentlessly: Drive tokens/sec, p99 latency, GPU utilization, and cost per token through quantization, batching strategy, and capacity planning.

• Engineer GPU-aware Kubernetes: GPU Operator, device plugins, GPU-aware scheduling, MIG partitioning, and distributed workloads over NCCL.

• Debug the hard layer: GPU OOMs, driver and CUDA runtime mismatches, interconnect bottlenecks, throughput regressions.

• Build the MLOps backbone: Model registry and versioning, CI/CD for AI workloads, and safe rollout/rollback.

• Own the evaluation layer: Offline and online eval harnesses, golden datasets, LLM-as-a-judge scoring, accuracy and hallucination metrics, and automated regression gates so no model, prompt, or quantisation change ships without a measured quality verdict.

• Make it observable: Response quality and accuracy drift, latency, throughput, GPU utilisation, and per-tenant cost — with SLOs, alerting, and incident response.

• Secure and multi-tenant by default: AuthN/AuthZ, secrets, data protection, tenant isolation, and governed access to models and GPUs.

• Codify and lead: Terraform for everything, HA and DR designed in, plus architecture direction and mentoring across teams.

• Contribute beyond AI: Design and build secure, multi-tenant microservices and APIs on AWS, run thorough code reviews, and own feature delivery end-to-end with Product and Design.

What We're Looking For:

• 2-4 years building and operating production software and infrastructure, with senior ownership of systems end to end.

• Bachelor's or Master's in Computer Science, Engineering, or equivalent practical experience.

• Strong Python and/or Go — you write and review production services, not just scripts and manifests.

• Deep Kubernetes and Docker: scheduling, resource management, operators, networking, debugging under load.

• Strong Linux, networking, storage, and distributed systems fundamentals.

• Production AWS/Azure/GCP experience — Lambda, API Gateway, EC2, S3, RDS and equivalents — with Terraform as your default way of working.

• Working knowledge of SQL and NoSQL databases, including schema design and performance tuning.

• Experience building and operating backend services and APIs in a multi-tenant SaaS product.

• Leadership, code review, and mentoring skills, with end-to-end ownership of delivery in an agile environment.

• Hands-on with a managed ML/AI platform — AWS SageMaker, Bedrock, GCP Vertex AI, or Azure ML — including where it fits and where self-managed infrastructure wins on cost or control.

• Track record on HA, scalability, and DR in multi-tenant environments.

• Solid observability and CI/CD practice — metrics, traces, SLOs, automated delivery.

• Experience building or operating AI evaluation systems — accuracy and quality measurement, LLM-as-a-judge pipelines, eval datasets, and regression testing for models and prompts.

• Background in platform engineering, infrastructure, SRE, distributed systems, AI infrastructure, or MLOps/LLMOps.

GPU & AI Experience (Required)

• Hands-on GPU cluster operations: provisioning, capacity planning, and day-2 ownership.

• NVIDIA ecosystem and CUDA — drivers, container runtime, toolkit compatibility, and their failure modes.

• GPU scheduling, allocation, isolation, and utilisation optimisation across competing workloads.

• Kubernetes GPU workloads: GPU Operator, device plugins, GPU-aware scheduling.

• GPU troubleshooting and tuning: memory/OOM, driver faults, interconnect and throughput bottlenecks.

• LLM inference and model serving in production (vLLM, Triton, KServe, Ray or equivalent) with demonstrated cost and performance gains.

• Managed AI platform experience — SageMaker (training jobs, endpoints, inference components) or equivalent — alongside self-managed GPU serving.

Nice to Have:

SageMaker HyperPod, Bedrock, or Vertex AI at scale · DCGM and Prometheus/Grafana for GPU fleets · MIG and GPU partitioning in multi-tenant setups · NCCL and distributed GPU workloads · quantization and inference optimization (FP8/INT8, AWQ/GPTQ, speculative decoding, KV-cache tuning) · LLM observability and tracing (Langfuse, OpenTelemetry for LLMs) · eval frameworks and human-in-the-loop labeling · PyTorch profiling · GPU cluster schedulers and queueing · vector databases, RAG, LLM gateways, agentic systems · TypeScript/Express API development · React or other modern frontends · security or compliance-bound environments.

What Success Looks Like:

• AI teams deploy and roll back models through self-service workflows, with no manual infrastructure work per deployment.

• GPU utilisation is measured, forecast, and consistently optimised across the shared fleet.

• Cost per token falls quarter over quarter, with the numbers on a dashboard.

• Inference SLOs hold under peak enterprise load, and GPU incidents drop in frequency and time-to-resolve.

• No model, prompt, or optimisation change reaches production without passing automated accuracy and quality evaluation.