AI / ML Engineer
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- Demonstrate ML foundations and production-software rigor
- Build retrieval and agentic systems that are measurable
- Operate safe, reliable and efficient AI in production
Who’s Hiring Right Now
US openings posted in the last 7 days at OpenAI — this role’s own benchmark employers — refreshed daily, as of 2026-11-01. Pulled directly from each company’s public job board; not exhaustive, and postings close fast — worth confirming the details on the employer’s own site before applying.
AI Engineer / ML Engineer / Applied AI Engineer
Benchmark: Production AI/ML loops: model evaluation + software engineering + retrieval/agents + serving/reliability 2026 interview refresh: High-demand interviews increasingly test production systems, evaluation, retrieval, latency/cost and failure analysis rather than model trivia alone.
Demonstrate ML foundations and production-software rigor
Model behavior + evaluation
Practice: validation, error analysis and failure taxonomy
Python/API + software quality
Practice: service boundaries, tests and reproducibility
Build retrieval and agentic systems that are measurable
RAG / grounding
Practice: retrieval quality, chunking, synthesis and citations
Agents + tool integrations
Practice: tool schemas, permissions, MCP and failure handling
Operate safe, reliable and efficient AI in production
Evals + regression gates
Practice: task success, groundedness, latency and cost
Security + adversarial robustness
Practice: prompt injection, jailbreaks and safe tool use
Interview-Readiness Proof / Practice Loop
Design and defend a production AI system. Include model choice, RAG/tooling, eval set, regression gates, latency/cost targets, observability, security boundaries, staged rollout and rollback. Expect interviewer-induced failure scenarios.
Pass Rubric
- ML judgment
- software rigor
- retrieval/agent fluency
- measurable evals
- failure analysis
- latency/cost tradeoffs
- security
- observability
- rollout/recovery
- concise system defense