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OpenAI

Partner Applied AI Engineer, Oracle and Cloud ISV

San FranciscoUnited States
FullTime · Go To Market
POSTED 11 NOVVERIFIED 10M AGO
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Role
0 days opentrue age
Closes in ~46d at the median (46d)
Posted 11 Nov · n=85
Payats field
$197k–278kposted
▼ −3% vs market median $245k
$197k$440k
Market p25–p75
$200k–$300k
OpenAI p25–p75
$265k–$440kn=37
H-1B median · ML / AI
$250k
SF Bay Area · n=553 · 365d to 11 Nov
CompanyOpenAI →
829open roles
Opened · 30d
+101
Closed · 30d
−77
10d of snapshots · 30d window
Market
3,319open · ML / AI
Opened · 7d
+2738% of open
All ml / ai
6,698open
All tracked boards · live 11 Nov · n=3,319
Visa
210H-1B LCAs · FY26
Median wage
$260k
UK licensed sponsor
✓Worker (A rating)
LCAs FY26 · n=210 · to 2026-06-30
SnowflakeLLMs

About the team

The Applied AI Engineering team helps developers and enterprises deploy generative AI safely and effectively. We serve as trusted technical advisors, helping customers and partners move from early experimentation to production-scale AI systems.

As a Partner Applied AI Engineer focused on Cloud ISVs, you will work with Oracle Cloud, Snowflake, and Databricks to help customers adopt OpenAI. You will bring OpenAI capabilities to the applications, data platforms, and developer workflows customers already use, then turn successful deployments into approaches the field and partner teams can deliver independently.

About the role

We are looking for an experienced technical leader to work with partner architects, AI specialists, and sales teams across three distinct routes to market. You will lead technical work from joint account discovery and architecture through evaluation and production, alongside OpenAI account teams and partner sellers. You will help customers choose the right deployment and purchasing path for the workload, with clear ownership of security, billing, support, and operations.

In this role, you will

Strategic partner engagement and influence

Build relationships with Oracle, Snowflake, and Databricks field leaders and technical teams. Shape joint account plans, identify the customer problem each partner can help solve, and prioritize opportunities with a clear path to production and reuse.

Oracle Cloud: Work with Oracle sellers on eligible, sales-managed Oracle Marketplace private offers for OpenAI API models, ChatGPT Work, and Codex. Distinguish procurement, billing, and eligible commitment drawdown from OpenAI-managed product access, inference, and support. Where customers build on OCI, design the application and data integration without assuming the model is hosted on OCI.

Snowflake: Work with Snowflake account and technical teams to qualify data and AI workloads where OpenAI models in Snowflake Cortex AI are a fit. Help customers evaluate use cases such as AI functions over governed data, search, analytics, and agents. Confirm model and regional availability, data boundaries, and operating requirements. Distinguish Snowflake-metered Cortex usage from a customer application that calls OpenAI directly.

Databricks: Work with Databricks sellers and solution architects on three joint plays: developer workflows with Codex and Unity AI Gateway, enterprise insights with ChatGPT and Genie where supported, and GPT-powered applications and agents with Mosaic AI Model Serving, Agent Bricks, and Databricks Apps. Validate each route against the customer’s governance and deployment requirements. Distinguish Databricks-billed usage from direct OpenAI billing and confirm commercial eligibility and attribution before making commitments.

Complex deal leadership and execution

Lead technical discovery, architecture, prototyping, evaluation, and production readiness. Agree with both account teams on what the customer will deploy, how success will be verified, and who owns the next step.

Validate model and feature availability, identity and network boundaries, data handling and residency, capacity, metering, and support ownership. Work with Alliances, Sales, and Billing Ops on technical readiness without taking ownership of pricing, terms, offer acceptance, or billing approvals.

Solution architecture and hands-on building

Build reference implementations with OpenAI APIs and partner-native capabilities, such as OCI application and data services, Snowflake Cortex AI, and Databricks Mosaic AI Model Serving. Use the appropriate integration path for the workload and validate permissions and data movement before deployment.

Put evaluation, observability, and deployment practices in place to measure quality, safety, reliability, latency, cost, and real workload usage.

Ecosystem enablement and scale

Create demos, workshops, reference architectures, and playbooks that partner engineers, field teams, and systems integrators can use independently. Pair on early deployments, then hand off proven patterns with clear prerequisites and support paths.

Source · OpenAIFirst seen 11 Nov1 editsOriginal ↗