Senior Software Engineer - Applied AI

Opplane provides data-focused solutions and is applying AI across the software delivery lifecycle. The Senior Software Engineer - Applied AI will design experiments, build classification and evaluation pipelines, analyze delivery outcomes, and translate findings into practical AI capabilities for engineering teams.

Responsibilities:

• Design and run experiments. Model tier routing, MCP coverage, permission configuration, repository context quality, budget headroom — randomized across teams and reported with their limits stated. These are the cleanly causal questions available once a tool is deployed, and where the returns are
• Own the analytical layer of the measurement program: work classification over model traffic, evaluation design, longitudinal within-unit analysis, and the staggered-adoption estimates that connect delivery outcomes to adoption timing
• Build and validate LLM-as-judge and classification pipelines — sampling strategy, hand-labeled ground truth, precision and recall measured and published, and revalidation whenever the taxonomy or the model changes
• Extend AI beyond code authoring into the stages that constrain delivery: test authoring and maintenance, environment and data setup, migration and modernization, code review assistance, security remediation, and evidence assembly for certification
• Work directly with the constrained teams. Where validation or certification is the bottleneck, coding assistance produces little regardless of how well it works — find where the constraint actually sits and aim the capability at it
• Build evaluations for internal AI capabilities: golden sets, regression suites, groundedness and answer-quality scoring, and the cost and latency telemetry beside them
• Turn findings into practice. Identify what the most effective practitioners do differently, document it, and teach it — publishing practices rather than rankings
• Partner with the platform team on Claude Code configuration, MCP servers, gateway telemetry, and the model registry, so what you learn becomes the default rather than folklore
• Report to engineering leadership and finance: what is working, what is not, where delivery is actually constrained, and what each claim does and does not establish