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Machine Learning System Engineer Jobs (NOW HIRING)

Senior Machine Learning System Engineer

Seattle, WA · On-site +1

$118K - $162K/yr

As a senior Machine Learning Systems Engineer on the Search Platform team, you will own and drive the design, development, and production deployment of machine learning systems that power search ...

Senior Machine Learning System Engineer

Seattle, WA · On-site +1

$118K - $162K/yr

Responsibilities As a senior Machine Learning Systems Engineer on the Search Platform team, you will own and drive the design, development, and production deployment of machine learning systems that ...

Senior Machine Learning System Engineer

Seattle, WA · On-site +1

$118K - $162K/yr

As a senior Machine Learning Systems Engineer on the Search Platform team, you will own and drive the design, development, and production deployment of machine learning systems that power search ...

Machine Learning System Software Engineer

Sunnyvale, CA · On-site

$203K - $240K/yr

As a Machine Learning System Software Engineer on the Apple Neural Engine (ANE) team, you'll work to bring high-performance, low-power AI solutions to life on iconic Apple products like the Vision ...

Machine Learning Engineer responsibilities include creating machine learning models and retraining systems. To do this job successfully, you need exceptional skills in statistics and programming. If ...

The Machine Learning Engineer will be an essential member of the Research and Development Team, where we engineer large tailor-made systems to solve complex data-related problems from many domains.

Description Quantum Machines (QM) is a global leader in quantum computing control systems. Through ... We are looking for a Machine Learning Engineer to design, build, and deploy machine learning ...

They are seeking a Machine Learning Engineer to create data platforms and pipelines for advanced ... Required : • Bachelor's Degree, (BA/BS) in Information Systems from a four-year college or ...

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Machine Learning System Engineer information

See salary details

$53.5K

$127.2K

$167K

How much do machine learning system engineer jobs pay per year?

As of Sep 8, 2026, the average yearly pay for machine learning system engineer in the United States is $127,215.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,000.00 and $157,000.00 per year, depending on experience, location, and employer.

What is a machine learning system engineer?

Machine learning system engineers are professionals who design, build, and maintain the infrastructure and systems that support machine learning models in production environments. They work at the intersection of software engineering and data science, ensuring that machine learning algorithms run efficiently, scale appropriately, and integrate seamlessly with existing applications. Their responsibilities often include data pipeline development, model deployment, monitoring, and optimization to ensure reliable and robust AI solutions.

What are the key skills and qualifications needed to thrive as a machine learning system engineer?

To thrive as a Machine Learning System Engineer, you need strong skills in computer science, statistics, machine learning algorithms, and a degree in a related field such as computer science or engineering. Proficiency with programming languages like Python or Java, experience with ML frameworks (e.g., TensorFlow, PyTorch), and knowledge of cloud platforms are typically required. Exceptional problem-solving abilities, teamwork, and effective communication are vital soft skills that help in designing scalable solutions and collaborating across teams. These skills ensure the successful development, deployment, and maintenance of reliable machine learning systems in real-world environments.

What are some common challenges machine learning system engineers face when deploying models to production environments?

Machine Learning System Engineers often encounter challenges such as ensuring model scalability, maintaining low latency, and addressing data drift once models are deployed in production. They must also work closely with software engineers, data scientists, and DevOps teams to integrate models seamlessly into existing systems and monitor their ongoing performance. Additionally, balancing computational resources and optimizing for cost efficiency while ensuring high reliability can be complex, making collaboration and clear communication essential in this role.

What is the difference between Machine Learning System Engineer vs Data Scientist?

AspectMachine Learning System EngineerData Scientist
CredentialsBachelor's or Master's in CS, ML, or related fields; certifications in ML or cloud platformsBachelor's or Master's in Statistics, Data Science, or related fields; certifications in data analysis or ML
Work EnvironmentDevelops, deploys, and maintains ML systems; collaborates with engineering teamsAnalyzes data, builds models, interprets results; works closely with business teams
Industry UsageTech companies, AI startups, enterprises deploying ML systemsResearch institutions, analytics firms, tech companies

While both roles involve machine learning, Machine Learning System Engineers focus on building and maintaining scalable ML systems, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in technical focus and responsibilities.

What cities are hiring for Machine Learning System Engineer jobs?

Cities with the most Machine Learning System Engineer job openings:

What are popular job titles related to Machine Learning System Engineer jobs?

For Machine Learning System Engineer jobs, the most frequently searched job titles are:

Infographic showing various Machine Learning System Engineer job openings in the United States as of September 2026, with employment types broken down into 1% As Needed, 85% Full Time, 12% Part Time, and 2% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $127,215 per year, or $61.2 per hour.

Senior Machine Learning System Engineer

Seattle, WA • On-site, Remote

Atlassian
Computer and Computer Peripheral Equipment and Software Wholesalers • 501 - 1,000 employees

$118K - $162K/yr

Full-time

Re-posted 10 days ago


Job description

Overview:

Working at Atlassian

Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.

Responsibilities:

As a senior Machine Learning Systems Engineer on the Search Platform team, you will own and drive the design, development, and production deployment of machine learning systems that power search experiences across Atlassian's product suite, including Jira, Confluence, and Rovo.

Search Platform Engineering
Design and implement scalable search serving infrastructure, including retrieval pipelines, vector indexing systems, and embedding-based semantic search. Own end-to-end delivery of ML components from experimentation through production rollout across multiple regions and tenants. Contribute to the architecture of high-throughput, low-latency search systems that meet strict SLO targets for availability, latency, and relevance quality.

ML Model Development & Serving
Build and maintain production ML models including neural rankers, embedding models, and reranking systems. Integrate models into serving infrastructure using frameworks such as Triton and PyTorch, ensuring reliability, scalability, and cost efficiency. Collaborate with ML researchers to translate experimental models into production-grade systems with robust monitoring and evaluation harnesses.

Agentic Search & Retrieval
Design retrieval systems purpose-built for agentic and RAG (Retrieval-Augmented Generation) use cases, including personalized indexes, grounding pipelines, and multi-step retrieval workflows. Partner with Rovo and AI platform teams to evolve search infrastructure as a foundational layer for AI agents, ensuring retrieval quality, freshness, and relevance at scale.

Operational Excellence & Cost Discipline
Drive observability, monitoring, and incident response for search serving systems. Apply FinOps principles to identify and execute cost optimization opportunities across vector search infrastructure and ML serving fleets. Maintain production health through rigorous on-call practices, runbook development, and proactive capacity planning.

Cross-Functional Collaboration
Work closely with engineering leads, product managers, and platform stakeholders to define technical roadmaps and deliver against team OKRs. Mentor junior engineers, contribute to design reviews, and champion engineering best practices across the team.

Compensation:

At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.

Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.

This role may also be eligible for benefits, bonuses, commissions, and equity.

Pay Ranges:

In The United States, we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are:

Zone A: $180,000 - $235,000

Zone B: $162,000 - $211,500

Zone C: $149,400 - $195,050

Qualifications:

Benefits & Perks

Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits.

About Atlassian

At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together.

We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines.

To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them.

To learn more about our culture and hiring process, visit go.atlassian.com/crh.

In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes.