About Us
VizopsAI is the secure runtime for custom enterprise software. We provide the production layer that turns AI-generated internal tools into compliant, hardened applications — wrapping raw AI code in enterprise-grade identity, security, and infrastructure best practices. We're a lean, fast-moving team building the industrialization layer for the AI app revolution.
We're an early stage venture-backed AI-native startup based in the SF Bay Area. The founding team combines deep AI/ML research leadership at Google DeepMind, Amazon Alexa, and Oracle Cloud with AI Product leadership at Verkada, AWS and Sony. Technical leadership includes PhDs from Johns Hopkins specializing in deep learning and optimization.
We already have multiple customers locked in and are bringing on rockstars to build the infrastructure that makes enterprise AI adoption safe and scalable.
Why Join VizopsAI
- Work at the frontier of enterprise AI infrastructure — identity, security, and runtime hardening
- Ship production systems that enterprises trust with their most sensitive workloads
- Small team, massive impact, no corporate bureaucracy
- Learn from PhDs and leaders from Google DeepMind, Amazon Alexa, Verkada
- Solve hard problems across authorization, network security, hybrid cloud, and AI optimization
Benefits
- Competitive salary and generous equity
- Healthcare coverage
- Learning budget for conferences and courses
- Work directly with founders and customers
Our Ethos
- Shipping over polishing — learn fast, iterate faster
- Bridge research and engineering — every experiment becomes real software
- Own what you build end-to-end — no silos, no handoffs
- Curiosity over credentials, action over consensus
- Lean, transparent, and ambitious
You'll own the engineering foundations for taking AI-powered workflows (including agentic systems) from prototype to reliable production. This is a high-ownership, hands-on role spanning distributed systems, ML systems, Kubernetes/cloud infrastructure, performance engineering, and enterprise-ready platform design. You'll build core platform capabilities (execution/runtime, data/model pipelines, APIs, observability, scaling) and ensure the system is fast, resilient, and easy to integrate into a customer's existing stack.
What You'll Do
- Design and build production services for AI/ML workloads with clear SLOs (latency, throughput, availability), including synchronous request paths and safe fallbacks
- Build a sandboxed execution runtime for running customer-provided or semi-trusted logic safely at scale (isolation boundaries, cold-start mitigation, warm pools, resource governance)
- Build and operate large-scale data + embedding/model pipelines (batch processing, feature generation, training data preparation, serving-friendly formats)
- Architect event-driven systems using Kafka-style streaming for ingestion, replayability, and decoupling offline pipelines from latency-sensitive online services
- Own Kubernetes deployments end-to-end: rollouts (blue/green, rolling), autoscaling, networking (ingress, gateways, service-to-service), resource tuning, and on-call grade debugging (OOM, crash loops, throttling)
- Build platform-grade observability: metrics, logs, traces, dashboards, alerting, and incident runbooks; instrument application-level profiling for memory/GC and performance regressions
- Implement multi-tenant API controls: API key management, quotas, rate limiting (token/leaky bucket), request scheduling/fairness, backpressure, and retry strategies with jitter
- Drive performance optimizations across the stack (parallelization, serialization/I/O bottlenecks, caching/batching), and rewrite hotspots when needed
- Build enterprise-ready integrations-first product surfaces (fit into Datadog/ServiceNow/identity/logging workflows instead of "one more dashboard")
- Partner with product and customers to translate ambiguous requirements into robust, developer-friendly platform primitives
What We're Looking For
- Strong experience building production distributed systems (service design, reliability patterns, scaling strategies, failure modes)
- Proven track record in performance engineering (profiling, concurrency/parallelism, bottleneck elimination, cost/perf tradeoffs)
- Deep hands-on experience with Kubernetes in production (deployments, networking, autoscaling, debugging, observability)
- Solid cloud fundamentals (AWS/GCP/Azure): compute lifecycle automation, networking, IAM basics, cost controls, and operational tooling
- Experience designing low-latency APIs and making pragmatic tradeoffs between latency, availability, and consistency
- Familiarity with streaming/event systems (Kafka/event sourcing) and building pipelines that support replay and auditability
- Strong programming skills (Python plus at least one systems language like Go/Rust/C++ is a plus)
- Comfortable operating in a fast-moving startup environment: clear communication, high ownership, and good engineering judgment
- 4+ years experience in backend/platform/infra roles (or equivalent depth)
Nice to Have
- Experience building sandboxed runtimes or isolation layers (microVMs, gVisor, containers, secure execution boundaries)
- Built large-scale embedding/recommendation or retrieval pipelines and served them in production
- Experience with multi-tenant platform concerns: noisy neighbor mitigation, quota enforcement, fairness scheduling, per-tenant observability
- Strong opinions on enterprise integrations and "platform adoption" mechanics (connectors-first, workflow-native design)
- Experience implementing safe progressive delivery for ML-backed systems (shadowing, canarying, rollback automation, regression gating)
- Experience with infrastructure-as-code (Terraform/CDK), CI/CD, and production readiness practices from scratch
You'll work across the stack to design, train, and deploy RL-driven optimization loops for AI agents. You need to be deeply technical, hands-on, and comfortable moving between research, ML engineering and production (APIs, infra, SLAs).
What You'll Do
- Design RL loops for multi-step, tool-using agents (planning, retrieval, coordination)
- Build backend services for training, evals, and online policy updates
- Train reward models from traces/preferences; run A/Bs & interleavings safely
- Scale distributed training/serving
- Turn papers → running code → measurable uplift
- Partner with product & customers: translate messy, real-world objectives into measurable rewards and robust policies
What We're Looking For
- Strong programming in Python; bonus for TypeScript/Go/Rust for systems and APIs
- LLM systems intuition including tool-use, planning, retrieval, structured outputs, and how evals/telemetry become learning signals
- You've shipped production systems and can debug/profile at speed
- Experimentation mindset: design clean evals, run ablations, read papers, and turn them into maintainable code
- Data & infra fluency: event/trace pipelines, schema design, reproducibility, and versioning for datasets, policies, and rewards
- Clear communication in a fast, ambiguous environment
- 2+ years experience building ML systems
Nice to Have
- RL proficiency with some real-world RL applications
- Experience with agent stacks: orchestration graphs, tool routers, retrievers, evaluation frameworks, and observability of traces
- LLM post-training experience: reward-modeling, preference data collection, safety/guardrail integration, structured evals
As a Member of Technical Staff (MTS), you'll build production-grade systems that power continuous optimization loops for AI agents—from evaluation pipelines and data/trace infrastructure to APIs that deploy improved policies. This role is a blend of MLE + backend engineering with a strong customer empathy component. You'll partner closely with customers and products to translate real-world objectives (accuracy, latency, cost, safety) into measurable signals and reliable services.
**Note:** Unlike our Founding AI Engineer role, there's no expectation to read/implement research papers from scratch—the bar for engineering rigor, ownership, and ambiguity-handling remains high. You'll need to demonstrate clear communication and high ownership in a fast, evolving environment.
What You'll Do
- Build backend services for training, evals, telemetry, and online policy updates
- Instrument observability to make optimization loops inspectable and reliable
- Translate customer KPIs into reward signals, guardrails, and success metrics
- Collaborate with product & customers to reduce time-to-uplift and land measurable improvements in production
- Scale distributed workloads for training/serving. Improve reliability, cost, and latency over time
What We're Looking For
- Strong programming in Python; comfortable with backend systems
- You've shipped production systems and can debug other people's code
- Fluency with data & infra - Containerization (Docker/K8s), cloud (GCP/AWS)
- 2+ years experience building ML or backend systems
Nice to Have
- Experience with RL, reward modeling, LLM evals, or agent stacks (retrievers, tool routers, orchestration)
- Familiarity with vLLM, LangSmith/Langfuse, SkyRL, Verl, Llama Factory, Agent Lightning
- LLM post-training exposure (preference data collection, safety/guardrails, structured evals)
Join our team to work on cutting-edge RL research for AI agents. You'll contribute to designing and testing RL-driven optimization loops while learning from experienced researchers and engineers. This is a hands-on role where you'll ship real code and see direct impact.
What You'll Do
- Assist in designing RL loops for multi-step, tool-using agents
- Help build and improve training pipelines and evaluation frameworks
- Run experiments with reward models and policy training
- Contribute to turning research papers into working prototypes
- Collaborate with the team on improving agent performance metrics
- Document experiments and share learnings with the team
What We're Looking For
- Proficiency in Python for ML/RL work
- Familiarity with LLMs and how they're used in agent systems
- Understanding of ML fundamentals and basic RL concepts
- Ability to read and implement ideas from research papers
- Strong coding skills and attention to detail
- Enthusiasm for learning and working in a fast-paced environment
- Currently pursuing or recently completed degree in CS, ML, or related field
Nice to Have
- Prior internship or project work with RL
- Experience with LLM APIs or agent frameworks
- Contributions to open source ML projects
- Familiarity with evaluation frameworks and observability tools
Serve as a force multiplier to the CEO at the intersection of strategy, operations, and execution. This is a strategic leadership role (not administrative) where you will own initiatives end-to-end and help scale the company. You will work directly with founders who have led teams at Google DeepMind, Amazon Alexa, and Verkada.
What You'll Do
- Drive strategic priorities and lead cross-functional special projects critical for growth
- Build and refine operating rhythms (OKRs, board, all-hands) to ensure alignment and speed
- Support fundraising, investor relations, and key partnerships to accelerate GTM
- Remove bottlenecks across hiring, finance, and product operations so teams can ship
- Bridge research, engineering, and business to prevent silos and keep execution tight
What We're Looking For
- 4+ years in management consulting, investment banking, VC, or high-growth startup operations
- High agency and bias to action; proactive problem-solver who moves without waiting for permission
- Structured thinker who can turn ambiguity into actionable plans
- Clear written and verbal communication; comfortable context switching across domains
- Versatile operator who can engage on RL/AI topics and financial/operational models
Nice to Have
- Prior chief of staff / founder associate experience
- Exposure to AI/ML or technical teams
A “learn by doing” role embedded in the Office of the CEO. You’ll wear many hats—market research, sales outreach, product testing, and ops—to help build a category-defining company from the inside out.
What You'll Do
- Execute critical operational tasks that keep founders focused on product and engineering
- Drive market intelligence on the AI agent landscape, customers, and competitors
- Support onboarding of early design partners and gather feedback into the product loop
- Draft memos, decks, and technical content to articulate the vision
- Jump into whatever is needed to unblock growth (“no task too small”)
What We're Looking For
- 0–2 years experience; recent grads or early-career candidates with a track record of excellence
- Hungry, ambitious, and comfortable with high intensity and ambiguity
- Fast learner and generalist; able to pick up new tools and concepts quickly
- Detail-oriented with strong ownership; nothing falls through the cracks
- Genuine passion for AI and excitement about agent optimization
Nice to Have
- Prior startup or founder associate experience
- Exposure to CRM, sales ops, or basic data analysis