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Location: San Francisco Bay Area

Type: Full-Time

Compensation: Competitive salary + meaningful equity (founding tier)

Backed by 8VC, we're building a world-class team to tackle one of the industry’s most critical infrastructure problems.

About The Role

Our AI Lab is pioneering the future of intelligent infrastructure through open-source LLMs, agent-native pipelines, retrieval-augmented generation (RAG), and knowledge-graph-grounded models.

We’re hiring an ML Ops Engineer to be the glue between ML research and production systems — responsible for automating the model training, deployment, versioning, and observability pipelines that power our agents and AI data fabric.

You’ll work across compute orchestration, GPU infrastructure, fine-tuned model lifecycle management, model governance, and security e

Responsibilities

• Build and maintain secure, scalable, and automated pipelines for:
• LLM fine-tuning, SFT, LoRA, RLHF, DPO training
• RAG embedding pipelines with dynamic updates
• Model conversion, quantization, and inference rollout
• Manage hybrid compute infrastructure (cloud, on-prem, GPU clusters) for training and
  
  

inference workloads using Kubernetes, Ray, and Terraform

• Containerize models and agents using Docker, with reproducible builds and CI/CD via
  
  

GitHub Actions or ArgoCD

• Implement and enforce model governance: versioning, metadata, lineage, reproducibility,
  
  

and evaluation capture

• Create and manage evaluation and benchmarking frameworks (e.g. OpenLLM-Evals,
  
  

RAGAS, LangSmith)

• Integrate with security and access control layers (OPA, ABAC, Keycloak) to enforce
  
  

model policies per tenant

• Instrument observability for model latency, token usage, performance metrics, error
  
  

tracing, and drift detection

• Support deployment of agentic apps with LangGraph, LangChain, and custom inference
  
  

backends (e.g. vLLM, TGI, Triton)

Desired Experience

Model Infrastructure:

• 4+ years in MLOps, ML platform engineering, or infra-focused ML roles
• Deep familiarity with model lifecycle management tools: MLflow, Weights & Biases, DVC,
• HuggingFace Hub
• Experience with large model deployments (open-source LLMs preferred): LLaMA,
• Mistral, Falcon, Mixtral
• Comfortable with tuning libraries (HuggingFace Trainer, DeepSpeed, FSDP, QLoRA)
• Familiarity with inference serving: vLLM, TGI, Ray Serve, Triton Inference Server
  
  

Automation + Infra:

• Proficient with Terraform, Helm, K8s, and container orchestration
• Experience with CI/CD for ML (e.g. GitHub Actions + model checkpoints)
• Managed hybrid workloads across GPU cloud (Lambda, Modal, HuggingFace Inference,
• Sagemaker)
• Familiar with cost optimization (spot instance scaling, batch prioritization, model sharding)
  
  

Agent + Data Pipeline Support:

Familiarity with LangChain, LangGraph, LlamaIndex or similar RAG/agent orchestration tools

Built embedding pipelines for multi-source documents (PDF, JSON, CSV, HTML)

Integrated with vector databases (Weaviate, Qdrant, FAISS, Chroma)

Security & Governance:

Implemented model-level RBAC, usage tracking, audit trails

Integrated with API rate limits, tenant billing, and SLA observability

Experience with policy-as-code systems (OPA, Rego) and access layers

Preferred Stack

• LLM Ops: HuggingFace, DeepSpeed, MLflow, Weights & Biases, DVC
• Infra: Kubernetes (GKE/EKS), Ray, Terraform, Helm, GitHub Actions, ArgoCD
• Serving: vLLM, TGI, Triton, Ray Serve
• Pipelines: Prefect, Airflow, Dagster
• Monitoring: Prometheus, Grafana, OpenTelemetry, LangSmith
• Security: OPA (Rego), Keycloak, Vault
• Languages: Python (primary), Bash, optionally Rust or Go for tooling
  
  

Mindset & Culture Fit

• Builder's mindset with startup autonomy: you automate what slows you down
• Obsessive about reproducibility, observability, and traceability
• Comfortable with a hybrid team of AI researchers, DevOps, and backend engineers
• Interested in aligning ML systems to product delivery, not just papers
• Bonus: experience with SOC2, HIPAA, or GovCloud-grade model operations
  
  

What We’re Looking For

Experience:

• 5+ years as a full stack or backend engineer
• Experience owning and delivering production systems end-to-end
• Prior experience with modern frontend frameworks (React, Next.js)
• Familiarity with building APIs, databases, cloud infrastructure, or deployment workflows at scale
• Comfortable working in early-stage startups or autonomous roles, prior experience as a founder, founding engineer, or a 0-1 pre-seed startup is a big plus
  
  

Mindset:

• Comfortable with ambiguity, eager to prototype and iterate quickly
• Strong sense of ownership — prefers to build systems rather than wait for tickets
• Enjoys thinking about architecture, performance, and tradeoffs at every level
• Clear communicator and pragmatic team player
• Values equity and impact over prestige or hierarchy
• Prior startup or founding team experience
  
  

Why This Role Matters

Your work will enable models and agents to be trained, evaluated, deployed, and governed at

scale — across many tenants, models, and tasks. This is the backbone of a secure, reliable,

and scalable AI-native enterprise system. If you dream about using AI to solve some really hard

real world problems – we would love to hear from you.