We’re looking for a hands-on ML engineer to prototype and productionize complex classification flows on real production logs, operate prompted classifiers at scale (ad hoc and scheduled), and build secure, compliant data-labeling pipelines.

We’re looking for someone with experience in data pipelines, data science, and machine learning, as well as a strong communicator and great teammate.

The right candidate takes the initiative and enjoys building world-class consumer experiences and products in a fast-paced environment.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more.

As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals.

Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond

Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.

This expectation is subject to local law and may vary by jurisdiction.

Responsibilities:

Build evaluation loops (precision/recall, calibration, drift, human-in-the-loop) and publish dashboards/SLOs.

Generalize machine learning (ML) solutions into repeatable frameworks.

Operationalize prompted classifiers at scale (batch & streaming), including orchestration, autoscaling, monitoring, and cost guardrails.

Conduct thorough review of data analysis and techniques used to summarize the process review and highlight areas that have been missed or need re-examining.

Collaborate cross-functionally with DS, Security, and Platform to define schemas, access patterns, and governance.

Independently write efficient, readable, extensible code and model pipelines.

Commit to a customer-oriented focus by acknowledging customer needs and perspectives, validating customer perspectives, focusing on broader customer context, and serving as a trusted advisor.

Qualifications:

Required Qualifications:

Bachelor’s Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Preferred Qualifications:

7+ years’ experience writing production-quality Python or Java or Scala code.

5+ years’ experience in distributed systems design and implementation of large scale data processing systems

3+ years’ experience building ML data pipelines using atleast one of AML, Promptflow, Langchain or LangGraph

Demonstrated interest in Responsible AI.

Experience prompting, evaluating, and working with large language models.

#MicrosoftAI #mai-datainsights #mai-datainsights

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We are seeking an experienced Engineering Manager to lead our Batch Compute Infrastructure team at Stripe. As a key member of our engineering organization, you will be responsible for defining the multi-year roadmap for Stripe's Batch Compute Infrastructure, leading complex architectural shifts and modernization.

Responsibilities

• Drive Strategic Vision: Define the multi-year roadmap for Stripe’s Batch Compute Infrastructure, leading complex architectural shifts and modernization.
• Lead and Scale: Build, mentor, and aggressively scale a high-performing team of engineers, proactively investing in their career development and fostering a culture of operational excellence.
• Ensure Operational Rigor: Maintain unwavering reliability for a Tier-0 infrastructure processing tens of thousands of daily workloads, proactively mitigating risks and managing complex on-call telemetry.
• Cross-Functional Orchestration: Collaborate deeply with data platform teams, finance, and user groups to define compute efficiency metrics, execute massive-scale cost optimization strategies, and guarantee compliance with global financial regulations.
• Technical Stewardship: Provide technical guidance in architecture reviews, evaluating critical cost, performance, and reliability trade-offs in distributed systems design involving Hadoop, Spark, AWS cloud primitives, and modern metastores.

Requirements

• 10+ years of professional software development and engineering experience.
• 3+ years of direct engineering management experience, successfully building and operating high-velocity technical teams.
• Deep technical background in building, scaling, and maintaining large-scale distributed data systems or Tier-0 infrastructure using open-source tools (e.g., Hadoop, Spark, Celeborn, Airflow, Kafka).
• Proven track record of driving significant infrastructure efficiency, managing capacity planning, and making data-driven cost-performance trade-offs.
• Experience working effectively in highly cross-functional, global organizations.

Preferred Requirements

• Experience managing remote or geographically distributed engineering teams.
• Familiarity with managing a massive fleet of Linux servers, on-premise Hadoop clusters, and modern cloud data architectures (e.g., AWS S3, Graviton).
• Demonstrated ability to navigate strategic ambiguity and deliver complex, multi-quarter infrastructural projects from inception to completion.
• Deep empathy for internal data users with a passion for building robust developer tooling and abstractions.

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As a senior machine learning engineer, you will play a key role in the end-to-end design and implementation of our product, which is a platform for powering use cases across training and serving of generative AI models. You will work closely with both customers and internal ML researchers to identify key areas of development for our generative AI platform.

Key responsibilities include:

• Designing and building the core platform infrastructure that supports our customer-facing product features
• Ensuring the reliability, security, and scalability of the backend distributed systems that power all aspects of our product
• Translating product requirements into user interfaces and backend distributed system design and owning end-to-end implementation

We look for:

• 4+ years of hands-on programming experience with at least one modern language such as Python, Scala, Go, or C++
• Strong sense of distributed systems design and experience building large-scale systems
• Experience building ML platform systems for applications in the ML model development lifecycle such as data preparation, model training, model evaluation, and model serving
• Direct experience developing ML models is a plus but not required
• Strong sense of end-to-end product ownership as well as intuition for both robust system design and product usability
• Effective communication skills and the ability to articulate complex technical ideas to cross-disciplinary internal and external stakeholders

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above.

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