Senior Applied ML Engineer - ML4Sys
Join Databricks as a Senior Applied ML Engineer to optimize infrastructure and enhance serverless compute products.
Posted by employer 2 months ago
First seen on Joblaze 2 months ago
Last verified on the company career page 1 day ago
Skills & Technologies
Requirements
- Experience
- 4+ years
- Education
- Master's degree
Not disclosed in this posting: work arrangement, visa sponsorship.
Joblaze summary
In the role of Senior Applied ML Engineer at Databricks, the individual will focus on enhancing the efficiency of the company's infrastructure through advanced machine learning and optimization techniques. Key skills include proficiency in Python, Scala, or Java, along with a solid understanding of cloud computing and distributed systems. This position is ideal for experienced professionals with a strong background in machine learning engineering, particularly those who have worked in fast-paced environments. The team is composed of domain experts dedicated to building innovative ML solutions that drive product performance.
Joblaze insights
- Listed about 2 months ago — first seen on Joblaze August 3, 2026. Last confirmed on Databricks's careers page October 7, 2026.
- Python appears in 52.8% of 536 comparable senior ai/ml roles in United States; data processing appears in 0.7% of 536 comparable senior ai/ml roles in United States.
Quick facts
- What's the salary range?
- Databricks lists $16,000–$21,000 for this role.
- How much experience is required?
- At least 4 years of relevant experience for this Senior Applied ML Engineer - ML4Sys role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: Java, Machine Learning, Python, Scala, cloud computing, data processing.
- What seniority level is this role?
- Databricks targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Senior Applied ML Engineer - ML4Sys role at Databricks.
From the original posting
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Summary
As a Senior Applied ML Engineer on the Applied AI team at Databricks, you will use machine learning, scheduling, and optimization algorithms to maximize the efficiency and performance of our infrastructure. Your work will span the entire stack—from cluster management down to query compilation. You will solve complex, high-impact engineering problems to deliver highly optimized, cost-effective workloads for our customers.
Impact You Will Have
- Accelerate Serverless Growth: Drive the scaling and efficiency of Databricks serverless compute products through advanced optimization techniques.
- Build Systems: Design end-to-end ML4Sys solutions from the ground up within a lean team of domain experts to support
- Shape Strategy: Define the roadmap for applied ML investments by collaborating with engineering and product leaders across Databricks.
- Drive Deployment: Architect, train, and deploy state-of-the-art models that directly improve product performance and cost efficiency.
- Scale Infrastructure: Build robust ML pipelines, data processing layers, model serving components, and production monitoring systems to help scale
- Innovate: Research and implement novel modeling techniques tailored specifically to computer systems and distributed environments.
Minimum Qualifications
- Education: Background in Computer Science and Master's degree in Machine Learning, Data Science, or a related computational field (AI, Bioinformatics, EE, Physics, etc).
- ML Experience: Strong background in building, training, and deploying machine learning models in production.
- Infrastructure Knowledge: Practical familiarity with cloud computing, distributed systems, and modern data processing frameworks.
- Core Coding: Proficiency in Python, Scala, or Java.
Preferred Skills
- Advanced Education: PhD in AI, Data Science, or a related technical discipline.
- Industry Experience: 4+ years of machine learning engineering experience in high-velocity, high-growth environment.
- Systems Domain: Strong understanding of computer architecture, distributed computing, cloud compute, database internals, or networking.
- Optimization: Experience with operations research, forecasting, markov decision processes, or other optimization algorithms for sequential decision making.
- Scale: Proven track record of optimizing large-scale distributed systems or cloud infrastructure via data-driven approaches.
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. For more information regarding which range your location is in visit our page here.
About Databricks
BenefitsStandard company text repeated across Databricks's postings is omitted here.