Principal Machine Learning Engineer (AI)

Location: San Francisco, CA; on-site at the Arcade office

Salary: Starting at $230,000 base salary, plus equity and competitive benefits

Company

hirify.global builds an MCP runtime, tools catalog, and tool-calling platform that enables AI agents to take secure, governed actions inside enterprise systems.

What you will do

• Own the end-to-end ML lifecycle, including data ingestion, training, evaluation, publishing, and reliable model releases.
• Train and fine-tune models for tool selection, routing, retrieval, recommendations, agent memory, embeddings, reranking, and classification.
• Build offline and online evaluation systems using real agent traces and compare models with Claude, GPT, Gemini, and other baselines.
• Quantize, optimize, package, and serve models in customer VPCs and air-gapped environments in collaboration with the Runtime team.
• Turn production agent traces and tool-call telemetry into privacy-controlled training data.
• Define the ML strategy and stack, make build-versus-buy decisions, and help shape future ML hiring and product direction.

Requirements

• 7+ years of software engineering experience, including 4+ years training and shipping production ML systems.
• Experience taking trained or fine-tuned models to production and improving customer-relevant metrics.
• Expertise in production agent systems, including harnesses, memory, skills, tool use, and sub-agents.
• Strong understanding of fine-tuning, training-data requirements, telemetry, statistical evaluation, and model quality measurement.
• Experience deploying models under latency, GPU, or infrastructure constraints using tools such as vLLM, ONNX, TensorRT, or llama.cpp.
• Strong Python for training and ML work, plus TypeScript or Go for production model-serving services.

Nice to have

• Experience shipping ML to enterprise, on-premises, or regulated environments.
• Tool-use benchmark or evaluation experience with BFCL, τ-bench, ToolBench, MCP evals, or equivalent.
• Familiarity with the MCP ecosystem and experience building privacy-controlled training-data pipelines from production traces.
• Previous experience as a first ML hire, at an early-stage startup, or contributing to open source or published engineering work.

Culture & Benefits

• In-person work from the San Francisco office.
• Competitive benefits and equity.
• Healthy budget for selecting and building the ML stack.
• Product-focused environment emphasizing shipping production systems over extended research cycles.
• Opportunity to work on AI infrastructure used by Fortune 100 customers.