The posting
Job Description
We made history and now we work to transform the future – for our customers, our communities and our families. You'll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.
Shape the Future of Manufacturing with ATP
At Ford, we aren’t just building vehicles — we are redefining how the world moves. Within the Advanced Industrial Technology and Platforms (ATP) organization, we are the architects of Ford’s next industrial revolution.
Our mission is clear: we build and scale industrial platforms, technologies, and capabilities that unlock Ford Firsts to drive sustained competitive advantage in manufacturing. Driven by our vision to "build once, scale everywhere," we bridge the gap between cutting-edge technology and global physical deployment.
Our guiding mantra is simple yet relentless: Scale what matters, win what's next.
If you are a high-tech builder, thinker, or innovator who thrives at the intersection of software, hardware, and massive scale, ATP is where you belong. Join us, and write the next chapter of industrial history.
In this position...
Ford is seeking a hands-on engineer to make enterprise data fast to connect, fast to retrieve, and fast to bring online. This role focuses on building the algorithms, architecture, and platform capabilities that connect data across domains, represent relationships effectively, and deliver data with predictable performance at scale.
You’ll work with diverse and evolving data sources, choosing representations and retrieval strategies that fit their structure and use. A key measure of success is reducing the time and effort required to connect a new source and make its data discoverable and usable—without creating fragile, one-off integrations. You’ll benchmark your choices against real workloads and make performance, connectivity, and onboarding speed explicit engineering priorities.
Responsibilities
What you'll do...
- Design and build the connectivity layer: develop reusable connectors, ingestion frameworks, and self-service onboarding tooling so that a new source can be registered, profiled, and queried with configuration rather than bespoke code.
- Model relationships across domains: design graph and relational schemas that link parts, plants, processes, suppliers, equipment, and quality events so that cross-domain questions can be answered by traversal rather than by hand-built joins.
- Engineer retrieval for speed: select and implement indexing strategies (adjacency, B-tree, LSM, inverted, bitmap, approximate nearest-neighbor) and query execution paths that meet defined latency and throughput targets as data volume grows.
- Own performance end to end: profile, benchmark, and tune the full path from source connection through ingestion, indexing, discovery, and retrieval, and treat it as a single performance and usability problem rather than a chain of separate systems.
- Build the discovery surface: integrate with metadata catalogs, schema registries, and lineage tracking so that every connected source is findable, documented, and traceable to its origin.
- Make trust a property of the platform: embed data quality checks, access control, governance policy, and lineage capture into the ingestion and retrieval path so that speed never comes at the cost of correctness or security.
- Instrument everything: define service-level objectives for retrieval latency, freshness, and onboarding time, and build the observability in Cloud Monitoring and Cloud Logging needed to measure and defend them in production.
- Operate what you build: deploy on Google Kubernetes Engine (GKE) and Cloud Run using Terraform, support the platform in production, and drive root-cause fixes rather than workarounds.
- Build once, scale everywhere: design so that a capability proven at one plant can be rolled out globally without re-engineering.
- Apply AI to automate the right parts of the work: use AI coding assistants and large language models for repetitive, well-specified steps such as scaffolding connectors, profiling sources, proposing schema mappings, and extending test suites, with validation and engineer review in the loop.
- Design self-service, AI-driven connector creation: build the product experience, APIs, and agent workflows that let internal customers describe a source, review an AI-generated connector and its tests, and deploy it through governed guardrails.
- Build retrieval for AI consumers: design embedding pipelines on Vertex AI, hybrid search across Vertex AI Vector Search and BigQuery, and graph-aware retrieval so that agents, copilots, and retrieval-augmented generation systems get accurate, governed, low-latency context from connected manufacturing data.
- Evaluate AI rigorously: set up evaluation harnesses and benchmarks for AI-assisted onboarding and retrieval quality, measure precision, recall, and latency against real workloads, and decide on evidence where automation is trustworthy enough to ship.
- Provide technical leadership: set the technical direction for connectivity and retrieval, author design documents, drive rigorous code and design reviews, and hold the team to a high standard for performance and reliability.
- Shape the roadmap: partner with data producers, analytics teams, and AI/ML teams to understand their access patterns and turn them into platform priorities.
How success is measured...
- Connected data is useful: relationships across sources and domains can be identified, navigated, and reused.
- Retrieval is fast and predictable: systems meet defined performance targets under realistic, growing workloads.
- The full path is optimized: source connection, ingestion, indexing, discovery, and retrieval are treated as one performance and usability problem.
- Speed doesn't compromise trust: data quality, security, governance, lineage, and reliability remain integral to the platform.
- New sources connect quickly: onboarding is measurable, repeatable, and increasingly automated rather than dependent on custom integrations.
Qualifications
You'll have...
- Bachelor's degree in a related field, or equivalent practical experience (5-7+ years)
- Professional experience building and operating data platforms, data infrastructure, or backend systems in production.
- Strong programming skills in at least one systems or data language such as Python, Go, or Rust, with a track record of shipping maintainable, tested code.
- Experience building reusable connectors, ingestion frameworks, or self-service source-onboarding capabilities. This includes designing software that non-platform users operate directly to bring their own sources online.
- Production experience with graph databases, graph processing frameworks, or graph query languages. Examples include Spanner Graph, Neo4j, TigerGraph, GQL, Cypher, Gremlin, or SPARQL.
- Experience with relational and columnar systems, document or key-value stores, search indexes, time-series platforms, or vector retrieval systems. Examples include BigQuery, Spanner, AlloyDB, Cloud SQL, Bigtable, Firestore, Milvus, or Vertex AI Vector Search.
- Knowledge of indexing approaches such as adjacency indexes, B-trees, LSM trees, inverted indexes, bitmap indexes, or approximate nearest-neighbor indexes. You can explain when each is the right choice and have used profiling and benchmarking to drive measurable performance improvements.
- Experience with metadata catalogs, schema registries, lineage, data discovery, or federated data-product platforms. Examples include BigQuery Analytics Hub.
- Familiarity with cloud infrastructure, Kubernetes, observability platforms, and infrastructure-as-code. Hands-on experience with Google Cloud Platform, including GKE, IAM, Cloud Monitoring and Logging, and Terraform, is expected.
- Experience in automotive, manufacturing, engineering, supply chain, or another complex, data-intensive environment.
- Practical experience applying AI coding assistants and agentic tools to automate specific parts of an engineering workflow, grounded in fundamentals that do not depend on them. You know which tasks AI handles well, and you verify what it produces.
Even better, you may have...
- Master's degree in Computer Science, Data Engineering, or a related field.
- Experience with streaming and change-data-capture pipelines (Pub/Sub, Dataflow, Datastream, Kafka, Flink, or similar) for near-real-time ingestion.
- Experience with industrial data sources and protocols such as OPC UA, MQTT, historians, or MES and PLM systems.
- Experience building retrieval infrastructure for AI/ML workloads on Vertex AI, including embedding pipelines, vector search, hybrid search, or retrieval-augmented generation.
- Experience applying large language models to data engineering tasks such as schema inference, entity resolution, data mapping, or automated data quality checks.
- Experience shipping AI-powered developer or data tooling to end users, such as code-generating agents, low-code integration builders, or natural-language-to-pipeline products, including the human review, sandboxing, and guardrails needed to make generated code safe to deploy.
- Experience building or integrating with agent frameworks and tool-use protocols such as Model Context Protocol, or exposing data platforms as tools for AI agents.
- Experience designing evaluation frameworks for LLM-based systems, including retrieval quality metrics and regression testing.
- Experience with query engines and open table formats such as BigQuery, BigLake, Dataproc and Spark, Trino, or Apache Iceberg.
- Experience designing data products or APIs consumed by multiple teams across a large enterprise.
- Strong written and verbal communication skills, including the ability to explain architectural trade-offs to non-technical stakeholders.
You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!
As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder…or all of the above? No matter what you choose, we offer a work life that works for you, including: • Immediate medical, dental, vision and prescription drug coverage • Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more • Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more • Vehicle discount program for employees and family members and management leases • Tuition assistance • Established and active employee resource groups • Paid time off for individual and team community service • A generous schedule of paid holidays, including the week between Christmas and New Year’s Day • Paid time off and the option to purchase additional vacation time.
This position is a salary grade 6 and ranges from $85,400-$143,200.
This position is a salary grade 7 and ranges from $99,600-$166,600.
This position is a salary grade 8 and ranges from $115,000-$192,900.
Final determination of salary grade will be based on candidate's skills and experience, and base salary will be set within the applicable range according to job scope, responsibility and competitive market value.
For more information on salary and benefits, click here: https://fordcareers.co/GSR
Visa sponsorship is available for this position.
Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire.
We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status. In the United States, if you need a reasonable accommodation for the online application process due to a disability, please call 1-888-336-0660.
This position is hybrid with a requirement to be onsite four or more days per week.
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About Us
At Ford Motor Company, we believe freedom of movement drives human progress. With our incredible plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career and help us define tomorrow’s transportation. We build world-class vehicles in our plants around the world, constantly developing new technologies and processes to further increase our efficiency. It’s our people who really make the difference. We encourage all of our employees to think outside the box to help us find better and more efficient ways to manufacture our vehicles.
