Senior AI Engineer, Location AI

Location/Hybrid Policy: Remote (from any city in Germany)

What you'll do

This role is scoped by skill rather than by product. The problems below span multiple departments and show up across our Search and Places data work, our Location and Navigation Intelligence work, and the Platform work that makes Mapbox usable by agents. You'll be hired into one specific team or product area, but you'll work across teams where your expertise meets the highest-priority AI problems. You should expect to move between teams and tech stacks as the work demands.

At this level you own the technical design and delivery of a multi-component AI system, and you are accountable for the quality of what ships in your area.

In this role, you will:

• Define how the products you own should work, build a measurement framework for it, and build evaluation systems for non-deterministic behavior. Formulate hypotheses around the products we build and seek the signal needed to validate them. Define what a correct result is for a given input and state, determine whether to assemble datasets from real usage or hand-written cases, and gate changes on regression results.

• Run continuous evaluation of the products we build, whether APIs, SDKs, data representations, or reference applications, from the position of the end user, whether developer, agent, or consumer. Assess the gaps such as misuse of parameters or integration anti-patterns, recommend the fixes, and make sure they land.

• Own the MVP against an agreed north star technical design, and balance technical perfection against shipping useful increments.

• Build data pipelines and the tooling around them: ingestion, conflation, entity resolution, quality checks, and the batch and streaming jobs that keep a large dataset current.

• Track and pull external datasets, models, and benchmarks from published research and open-source releases. Evaluate what fits the problem and constraints , and decide when to adopt what exists versus build your own.

• Design feedback loops so that using a product generates data that improves it. Instrument systems so failures arrive with enough context to reproduce, then turn the recurring ones into evaluation cases.

• Design the boundary between a model and the tools it calls. Build or improve the model harness, decide what the model handles, what it delegates, and how to keep it working from the state it actually fetched.

• Work to a latency and cost target per request: streaming, partial results, caching, model routing, prompt structure.

• Build the internal harnesses and tools (CLI, MCP, and others) your team needs to iterate quickly, and share the parts that generalize with other teams.

• Raise the bar on your team through code and design review, and bring other engineers up on eval practice.

• Some of the technical questions in this area are still open. You will help answer them.

• Participate in an on-call rotation to ensure our systems remain available to customers 24/7. Team members alternate as the on-call primary responder, which may require immediate response outside normal working hours, including weekends.

What We Believe are Important Traits for This Role

• Required Education/Certification:

  • Bachelors Degree in STEM discipline and 5+ years of software engineering experience, with production ownership of services, pipelines, or SDKs.

• Technical Skills & Tools (Must-Haves):

  • 2+ years shipping LLM-backed features to real users, in systems that carried error budgets, on-call rotations, and customers who noticed regressions.

  • Data engineering depth: SQL, at least one distributed processing framework, and experience with pipelines where a wrong record mattered more than a slow one.

  • Fluency with tool calling and agent orchestration, including the failure modes: stale context, hallucinated arguments, silent partial success, unbounded loops.

  • Strong Python or TypeScript, and comfort reading code in whatever language the caller happens to be written in.

• Core Competencies & Scope:

  • Direct experience or deep understanding of designing evaluations for non-deterministic systems. You can describe a dataset you built and the failure it caught.

  • Working knowledge of more than one agent harness, and opinions about where each of them is weak.

  • Experience diagnosing latency in a distributed request path.

  • Comfort with ambiguity, and the judgment to ship something narrow that works while the general solution is still unclear.

Nice to Have Traits for this Role

• Geospatial data experience: routing, geocoding, POI or address data, OpenStreetMap, or conflation of overlapping sources.

• Public API or SDK design experience, particularly for developers you never talk to.

• Experience building against MCP or similar tool transports.

• Experience running evals in CI, with a commercial harness or one you built.

• Automotive, in-vehicle infotainment, CarPlay, or Android Auto experience.

• Voice pipeline experience: streaming ASR, TTS, barge-in, endpointing, wake word.

• Work under constrained compute, offline, or intermittent connectivity.

• Experience operating a product through its first external integrations, where the customer finds the gaps before you do.

How we support you

• Hybrid/Remote Options: Enjoy flexibility to work comfortably from home or periodically from an office where applicable.

• Country-Specific Care & Coverage: Private health, dental, and income protection plans tailored to elevate your regional statutory benefits.

• Family-First Support: Family care, maternity and paternity leave policies to support your growing family.

• Lifestyle Spending Account: Contributions to support your health, wellness, and personal growth

• Balance & Brainpower: Mental health support for you and your dependents.

• Rest & Recharge: Paid time away, company holidays, and generous absence policies.

• Time Off to Give Back: Dedicated paid volunteering time in addition to your standard PTO.