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Software Engineer Ai Model Training Jobs (NOW HIRING)

Orbifold AI

Research Engineer (Los Altos)

Los Altos, CA · On-site

$250K/yr

Experiment with SOTA models and data curation techniques to maximize AI training efficiency and ... Industries Software Development Referrals increase your chances of interviewing at Orbifold AI by ...

Gallatin AI, Inc

AI Software Engineer

El Segundo, CA · On-site

$80K - $210K/yr

About the Role We're looking for an AI/ML Software Engineer to play a foundational role in ... Establish scalable MLOps pipelines and real-time inference services to streamline model training ...

Delos Data Inc

System Software Engineer - AI

Palo Alto, CA · On-site

$140K - $200K/yr

System Software Engineer - AI About us: We are a stealth‑mode startup building foundational ... models. * Identify and resolve performance bottlenecks in distributed training and inference ...

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Software Engineer Ai Model Training information

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$63.5K

$147.5K

$205.5K

How much do software engineer ai model training jobs pay per year?

As of Sep 10, 2026, the average yearly pay for software engineer ai model training in the United States is $147,524.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,000.00 and $173,000.00 per year, depending on experience, location, and employer.

What does a software engineer AI model training do?

A Software Engineer specializing in AI Model Training is responsible for designing, developing, and optimizing machine learning models. Their work involves preparing and processing large datasets, selecting appropriate algorithms, implementing training pipelines, and evaluating model performance. They collaborate closely with data scientists and other engineers to ensure that AI models are accurate, efficient, and suitable for deployment in real-world applications. Additionally, they may help maintain infrastructure for model training and contribute to research and development of new AI techniques.

What are the key skills and qualifications needed to thrive as a software engineer AI model training?

To excel as a Software Engineer in AI Model Training, you need strong programming skills (especially in Python), a solid grasp of machine learning fundamentals, and typically a degree in computer science or a related field. Experience with frameworks like TensorFlow or PyTorch, familiarity with data processing tools, and sometimes certifications in AI or ML are highly valuable. Analytical thinking, problem-solving, and effective collaboration enhance your ability to develop and refine complex AI models. These skills ensure that AI solutions are robust, scalable, and aligned with organizational goals in a rapidly evolving technological landscape.

What are some common challenges faced by software engineers AI model training, and how can they be addressed?

Software Engineers focusing on AI model training often encounter challenges such as managing large datasets, ensuring data quality, and optimizing model performance. Addressing these issues typically involves close collaboration with data scientists, domain experts, and DevOps engineers to streamline the data pipeline and refine training processes. Staying up to date with the latest advancements in machine learning frameworks and tools can also help overcome technical hurdles. Regular code reviews and cross-functional meetings further support problem-solving and foster a productive work environment.

What is the difference between Software Engineer Ai Model Training vs Data Scientist?

AspectSoftware Engineer Ai Model TrainingData Scientist
Required CredentialsBachelor's in CS, related field; experience with ML frameworksBachelor's or higher in CS, statistics, or related field; strong analytical skills
Work EnvironmentDevelopment teams, AI labs, cloud platformsData analysis, research environments, business units
Employer & Industry UsageTech companies, AI startups, research institutionsTech firms, finance, healthcare, consulting

While both roles involve working with data and machine learning, Software Engineer Ai Model Training focuses on developing and optimizing AI models through coding and engineering practices. Data Scientists analyze data, build models, and generate insights. The roles often collaborate but differ in their core responsibilities and skill sets.

Can I get paid to train AI models?

Yes, software engineers and AI specialists can be paid to train AI models, especially in roles that involve developing, fine-tuning, and optimizing machine learning algorithms. These positions often require knowledge of programming languages like Python, experience with machine learning frameworks, and access to computational resources. Compensation varies based on experience, location, and the complexity of the models being trained.

How to become a software engineer AI model trainer?

To become a software engineer AI model trainer, you should have a strong background in computer science, programming skills in languages like Python, and experience with machine learning frameworks such as TensorFlow or PyTorch. Gaining knowledge in data preprocessing, model evaluation, and working with large datasets is essential, along with relevant certifications or advanced degrees in AI or related fields.
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Software Engineer Ai Model Training jobs near you
Infographic showing various Software Engineer Ai Model Training job openings in the United States as of September 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 88% Physical, 1% Hybrid, and 11% Remote job distribution, with an average salary of $147,524 per year, or $70.9 per hour.

Staff Software Engineer - AI Research Infrastructure

New York, NY • On-site

Databricks
Software Development • 5 - 10K employees
Learn more about Databricksexternal

$190K - $270K/yr

Full-time

Re-posted 12 days ago

Key responsibilities

  • Design and implement infrastructure that supports large-scale experiments, data processing, and model training

  • Enable researchers to quickly run large-scale experiments by building abstractions for job submission, scheduling, and monitoring

  • Create tooling to improve research developer productivity, such as experiment management systems and CI/testing infrastructure

Job description

Staff Software Engineer - AI Research Infrastructure
P-1215
At Databricks, we are obsessed with enabling data teams to solve the world's toughest problems, from security threat detection to cancer drug development. We do this by building and running the world's best data and AI platform so our customers can focus on the high-value challenges that are central to their own missions.
The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. Databricks AI does so by producing novel science and putting it into production. Databricks AI is committed to the belief that a company's AI models and agents are just as valuable as any other core IP, and that high-quality AI should be available to all.
Job Description
As a Staff Software Engineer, AI Research Infrastructure, you will be developing and running the research stack that powers Databricks AI Research. You will design and build services that schedule, orchestrate, and observe large-scale training and inference experiment workloads across thousands of GPUs, improve our dev tooling and ensure that researchers can iterate quickly without sacrificing reliability, efficiency, or security.
You'll partner closely with research scientists, ML engineers, and platform teams to turn experimental workloads into robust, repeatable pipelines, and to push the limits of what our infrastructure can support.
The Impact you will have
As a Staff Software Engineer on the AI Research Infra Team at Databricks, you will:
  • Design and implement infrastructure that supports large-scale experiments, data processing, and model training (e.g., HPC clusters, GPU fleets, or cloud-based systems)
  • Enable researchers to go from idea to large-scale experiment in minutes, not days, by building powerful abstractions for job submission, scheduling, and monitoring.
  • Create tooling that improves research developer productivity, such as experiment management systems, CI/testing infrastructure for research code, and workflows that reduce iteration time.
  • Influence the long-term roadmap for research computation, shaping how Databricks AI Research train, evaluate, and ship models to customers.
  • Serve as a technical mentor and force multiplier for other engineers working on compute, infra, and AI systems.

What We Look for
  • BS/MS or PhD in Computer Science or related field
  • 5+ years of software engineering experience, including substantial time working on large-scale distributed systems or infrastructure.
  • Have deep experience with building and operating distributed systems, data pipelines, or large-scale backend services, ideally involving GPUs, clusters, or major cloud providers.
  • Are proficient in one or more systems programming languages (e.g., C++, Rust, Go, Java, Scala) and can design, implement, and debug complex services.
  • Have built or significantly contributed to cluster schedulers, resource managers, or large-scale job orchestration systems (e.g., Kubernetes, Slurm, Ray, custom internal systems).
  • Understand modern ML training and inference workflows (e.g., distributed training, model parallelism, fine-tuning, evaluation), even if you're not primarily a research scientist.
  • Can move fast and be pragmatic in getting things done, while caring about operational excellence. Have driven complex systems from prototype to stable, well-owned services.
  • Communicate clearly with both researchers and engineers, and enjoy translating between research needs and infra realities.

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.
Local Pay Range
$190,000-$270,000 USD
About Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide - including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 - rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.
BenefitsAt Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

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