Hiring: Senior Software Engineer - Applied AI Company: Opplane Location: San Francisco Bay Area Job Posted Time: 2026-09-16 20:02:14 Target Skills & Keywords : AWS, Airflow, Assembly, CI/CD, Change Management, Confluence, Dagster, Digital Transformation, Git, GitLab, GitLab CI, Jira, LLM, LangChain, Machine Learning, Model Registry, OpenTelemetry, Pandas, Product Management, Python, REST, Risk Management, SQL, dbt, scikit-learn About the job Experience: •7+ years spanning software engineering and quantitative analysis. This role needs both; a strong background in one and a passing acquaintance with the other will not carry it. Required Skills: •Opplane is applying AI across the software delivery lifecycle - not only to writing code, but to testing, review, documentation, migration, incident response, and the validation stages where delivery is usually constrained. Measuring what that produces is the starting project, not the whole of it. •Own the analytical and applied half of that work: designing the experiments that establish what actually helps, building the classification and evaluation pipelines the measurement program depends on, and then taking the findings back into the delivery lifecycle as capabilities teams can use. •This role pairs with a data engineer who owns extraction, identity, and the metric pipeline. You own what the numbers mean and what to do about them. •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. Qualifications: •Production experience with LLM applications: prompting, tool and function calling, context management, evaluation, and knowing where models fail in practice. •Experimental design and causal inference — randomized and quasi-experimental designs, difference-in-differences, instrumental variables, hierarchical models — and the judgment to say when a design does not support the claim being asked of it. •Strong Python and SQL, with a statistical stack (pandas, statsmodels, scikit-learn, or R). Data collection: instrumenting and extracting from operational systems and APIs, designing sampling that survives scrutiny, and knowing when a source cannot answer the question being asked of it. •Aggregation: resolving identity across systems, joining sources never designed to be joined, and modeling the summary tables reporting reads from. You need not own the pipeline, but you must be able to build one when the answer depends on it. •Analytics: exploratory analysis, distributions rather than averages, cohort and time-series work, and reports that state their own coverage and limits. •Real familiarity with the software delivery lifecycle — code review, CI/CD, test strategy, release and change management — sufficient to hold a credible conversation with the teams you are measuring. •Git and GitLab at instrumentation depth: merge request and pipeline data models, diffs and SHAs, what merge, squash, rebase, and cherry-pick do to line-level analysis, and the API and hook surfaces available for capturing it. •Jira and Confluence integration experience — REST APIs, changelog and page version history, the GitLab–Jira development panel, and the field and label conventions that determine whether the resulting data means anything. •Care with personnel-adjacent data: aggregate reporting by default, and a clear sense of what should not be built even when it is technically easy. •Communication that works in both directions — an executive audience that wants a number, and engineers who will dispute it. Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!