Machine Learning Engineer Intern; Conversational AI PhD

Location:

Seattle

Employment Type:

Intern

Job Code:

A218361A

• Agent runtime (harness / agent loop). Orchestrate skills, tools, and context; implement loop control & intervention, progressive disclosure, and behavior-level guardrails. Build the production safety layer — pre-flight budgets and timeout truncation, serve-time gates, shadow / swap-in answer delivery, and safe fallback paths.
• Context & memory for long multi-turn agents. Agentic memory (structured note-taking), context compaction / summarization, context editing / observation masking, and just-in-time (retrieve-then-load) retrieval. Treat context as an evolving, itemized playbook — with structured diffs and a deterministic curator — rather than an ever-growing prompt.
• Post-training & the data flywheel. SFT / DPO / RL to internalize rules into weights (so the prompt gets shorter, not longer), plus distillation to smaller serving models. Turn served conversations into training / eval / retrieval signals.
• Tools, Skills, and MCP. Tools-as-APIs, connectors, skill / tool search for large inventories, and skill-library governance — description conflicts, trigger evals, cross-skill mis-fire matrices, and on-demand loading instead of dumping every definition into context.
• Evaluation you can bet a launch on. LLM-as-judge with human-agreement calibration; statistical rigor — paired comparison, confidence intervals, repeated sampling, pass^k; held-out and time-rolling eval splits with overfitting alarms; cascaded scoring and cross-family judge panels to make evaluation affordable at scale.
• The self-evolving loop. Case mining → automatic root-cause → constrained candidate generation → replay verification against frozen regression sets → canary → flywheel. Build the plumbing that makes it auditable: candidate registry with exact runtime read-back, change lineage, and an archive of rejected candidates you can sample from next round.
• Online experimentation & causal readout. Shadow / canary / A-B, non-inferiority gates, traffic-split health, metric definitions that survive scrutiny, and off-policy counterfactual evaluation where live A/B isn't possible.

• Currently pursuing a PhD in Computer Science, Engineering, Operations Research or a related technical discipline
• Strong Python plus one of C++ / Go / Rust / Java
• Solid ML / DL / NLP fundamentals, with genuine hands-on experience with LLMs or agents (coursework, research, internship, competition, open-source, or a serious side project)
• Basic statistical literacy — you can compute a confidence interval, explain what a p-value does and doesn't mean, and tell the difference between "the number went up" and "the system got better"
• Able to read a paper or an engineering blog and turn it into working code
• Preferred Qualifications
  - Hands-on experience in any one of the following — depth in one is enough, breadth welcome:
• Post-training: SFT / DPO / RLHF / RLAIF / RLVR, reward modeling, reward hacking and how to defend against it
• Agent systems: harness, context engineering, MCP / Skills, sub-agents, tool search
• Evaluation & experimentation: LLM-as-judge and judge calibration, pass^k, regression suites, A/B and non-inferiority testing, off-policy evaluation
• Self-improving / evolutionary systems: evolutionary program search, candidate archives and parent sampling, automatic prompt / context optimization, multi-objective (Pareto) selection and credit assignment
• Publications (for PhD), strong competition results (ACM-ICPC / Kaggle / ML competitions), or notable open-source contributions
• E-commerce or multilingual experience is a plus, not required

The base salary range for this position in the selected city is $57…