Full-Time
Manager – Machine Learning Practice
Posted on 6/6/2025
Databricks
10,001+ employees
Data lakehouse platform for analytics
No salary listed
Bengaluru, Karnataka, India
Remote
Preference for candidates located in Bangalore, India.
 AI & Machine Learning (1) |
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People at Databricks
People at Databricks who can refer or advise you
- 3+ years experience managing, hiring, and building a team of motivated data scientists/ML engineers, including establishing programs and processes
- Deep hands-on technical understanding of data science, ML, GenAI and the latest trends
- Experience building production-grade machine learning deployments on AWS, Azure, or GCP
- Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
- Manage hiring, onboarding, and growing the ML Practice team in India, roughly doubling the team size by the end of the year
- Develop relationships with key customers and partners, scope customer engagements, and manage escalations
- Align with Professional Services and Sales leaders both in the Global Delivery Center and globally
- Collaborate cross-functionally with the R&D team to define priorities and influence the product roadmap
- Lead strategic PS initiatives, practice development, and processes
- Own OKRs for revenue and utilization, with a focus on driving Databricks consumption
- Lead Databricks cultural values by example and champion Databricks brand
- Experience working with Databricks and Apache Spark
- Experience working in a customer-facing role
Databricks provides a unified data and AI platform built around a lakehouse architecture that blends data lakes and data warehouses. It helps organizations ingest, store, manage, and analyze data from various sources, then apply analytics and machine learning at scale. The platform offers automated ETL, secure data sharing, and high-performance analytics, with built-in support for AI workloads and model deployment. Unlike traditional single-purpose data stores, Databricks combines data engineering, data science, and business analytics in one system, aiming to streamline data workflows and make insights readily actionable. Its goal is to enable businesses to manage data more efficiently, accelerate insight generation, and deploy AI and analytics across diverse teams through a subscription-based platform and professional services.
Company Size
10,001+
Company Stage
Debt Financing
Total Funding
$27.1B
Headquarters
San Francisco, California
Founded
2013
People at Databricks
People at Databricks who can refer or advise you
Simplify's Take
What believers are saying
- Annualised revenue run rate reached $6.9 billion, surpassing Snowflake's $5.5 billion in scale.
- 80% of databases are now created by AI agents, driving demand for operational databases.
- Partner ecosystem including Curinos and Dun & Bradstreet delivers rapid AI monetization and bad debt avoidance.
What critics are saying
- Microsoft Fabric displaces Databricks in Azure-committed enterprises within 12–18 months with 50–70% probability.
- LTAP's 'zero copies' claim faces 60–80% probability of audit failure due to confirmed dual physical data shapes.
- Hyperscalers like Google BigQuery cut TCO by 30–50% with native Iceberg and managed Spark, bypassing Databricks.
What makes Databricks unique
- Unified lakehouse architecture merges data lakes and warehouses on open formats like Apache Spark.
- Unity Catalog serves as an agent system of record for enterprise AI governance and context.
- Native integration of ETL, machine learning, and AI tools simplifies end-to-end data workflows.
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Benefits
Extended health care including dental and vision
Life/AD&D and disability coverage
Equity awards
Flexible Vacation
Gym reimbursement
Annual personal development fund
Work headphones reimbursement
Employee Assistance Program (EAP)
Business travel accident insurance
Paid Parental Leave
Growth & Insights and Company News
6 month growth
↑ 0%1 year growth
↑ 0%2 year growth
↓ -1%RadixArk announces $100 million in Seed funding at a $400 million post-money valuation.
Persistent Systems has launched a Merchant Risk Management and Fraud Detection solution powered by Databricks' Data Intelligence platform. The solution uses Agentic AI to perform real-time merchant vetting during onboarding and continuous monitoring of transactions, chargebacks and third-party signals to detect fraud and compliance risks. Built on Databricks' platform, the solution triggers configurable actions when risk signals are detected, including enhanced monitoring or transaction restrictions. Persistent expects the solution to deliver a 20–40% reduction in fraud losses, 30–60% improvement in detection accuracy and 50–70% reduction in manual review effort. The solution is available now as a Databricks accelerator for banks, acquirers and payment service providers globally. Persistent is a Databricks Global Systems Integrator partner with over 900 certified professionals.
Databricks co-founder and CTO Matei Zaharia has been awarded the 2026 ACM Prize in Computing, recognising his contributions including Apache Spark, the open source big data project he created during his PhD at UC Berkeley in 2009. The award includes a $250,000 prize, which Zaharia is donating to charity. Under Zaharia's engineering leadership, Databricks has grown into a cloud storage and AI data foundation giant, raising over $20 billion at a $134 billion valuation and achieving $5.4 billion in revenue. Zaharia believes artificial general intelligence already exists but argues we should stop applying human standards to AI models. He advocates for AI agents that leverage their unique strengths in data processing rather than mimicking human assistants, citing security risks. His focus is on AI-powered research automation across fields like biology and engineering.
DBOS, Inc. has announced a technology partnership with Databricks to enhance the reliability and observability of agentic AI workflows. The collaboration integrates DBOS's open-source durable execution platform with Databricks and Lakebase, Databricks' serverless Postgres database designed for AI agents. The partnership addresses challenges in agentic AI, including long-running workflows and unpredictable model responses. DBOS stores workflow checkpoints in Lakebase in real time, enabling AI agent workflows to resume automatically after failures without data loss. Yutori, an AI company building autonomous web agents, is already using the combined platform to power its workflows. The integration is available immediately and requires no additional infrastructure or coding changes. DBOS is backed by joint MIT-Stanford research and serves AI startups and Fortune 100 companies.
Trent AI, a London-based AI security startup, has launched with $13 million in seed funding led by LocalGlobe and Cambridge Innovation Capital. Executives from Databricks and Stripe also participated. Founded by former Amazon Web Services engineers Eno Thereska, Neil Lawrence and Zhenwen Dai, Trent AI has developed a platform that uses AI agents to identify cybersecurity issues in AI agents and their generated code. The platform employs four groups of agents that find exploits, rank vulnerabilities by severity, generate remediation suggestions, and track security changes over time. The platform can simulate complex attack paths and provides tool-specific features for OpenClaw and Lovable. Trent AI claims its technology outperforms traditional cybersecurity products designed for conventional software. The company will use the funding to expand its customer base and engineering team.