ML Software Engineer Jobs
ML Software Engineer jobs are open across technology, finance, healthcare, and autonomous systems, from new-grad to principal and staff levels, with specializations in NLP, computer vision, and MLOps. Find a role that fits from the openings below and apply directly.
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Software Engineer, Systems ML - Compilers / Backend Responsibilities:
- Analyze and design effective compiler passes and optimizations. Implement and/or enhance code generation targeting machine learning accelerators
- Work with algorithm research teams to map ML graphs to hardware implementations, model data-flows, create cost-benefit analysis and estimate silicon power and performance
- Work with hardware architects to co-design hardware features that maximize performance, power efficiency and programmability
- Contribute to the development of machine-learning libraries, intermediate representations, export formats, and analysis tools
- Analyze and improve the efficiency, scalability, and stability of our toolchains. Optimize and tune kernels and compiled code to achieve latency targets for ML inference
- Conduct design and code reviews. Evaluate code performance, debug, diagnose and drive resolution of compiler and cross-disciplinary system issues
- Interface with other compiler-focused teams to evaluate and incorporate their innovations and vice versa
Minimum Qualifications:
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- 2+ years experience developing compilers, toolchains, runtime, or similar code optimization software
- Experience in software design and programming experience in Python and/or C/C++ for development, debugging, testing and performance analysis
- Experience in AI framework development or accelerating models on hardware architectures (GPU, TPU, custom AI ASICs)
Preferred Qualifications:
- Experience of developing in a mainstream machine-learning framework, e.g. PyTorch, MLIR, Tensorflow or Caffe
- Experience with machine-code generation or compiler back-ends for on-device inference workloads
- Experience working and communicating cross functionally in a team environment
- Experience working on and contributing to an active compiler toolchain codebase, such as LLVM, MLIR, GCC, MSVC, Glow
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Experience in deep learning algorithms and techniques, e.g., convolutional neural networks, recurrent networks, etc
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience developing high-performance kernels or runtime components and tuning them for inference specific accelerator platforms
About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$154,003/year to $217,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.
ML Software Engineer Jobs by Experience Level
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Who's Hiring
- Apple21

- Meta20

- Amazon Web Services16

- Google13

- Zoox8

Top Industries Hiring
- Technology & Software48
- Electronics & Hardware18
- Automotive9
- Artificial Intelligence7
- Banking & Financial Services5
What Employers Look For
The qualifications that appear most often in ML software engineer jobs.
- Proficiency in Python and at least one major ML framework such as PyTorch or TensorFlow
- Experience designing, training, and deploying machine learning models in production environments
- Familiarity with MLOps practices including experiment tracking, model versioning, and CI/CD pipelines
- Strong foundations in statistics, probability, and linear algebra relevant to model development
- Bachelor's or master's degree in computer science, electrical engineering, or a related quantitative field
- Experience with cloud platforms such as AWS, Google Cloud, or Azure for scalable model serving
Tips for Your ML Software Engineer Job Search
Quantify model impact on your resume
Hiring managers want to see what your models actually did. Replace vague descriptions with outcomes: latency reductions, accuracy gains, or throughput improvements. Concrete metrics on your resume make it past automated screens and give interviewers something specific to dig into.
Tailor your GitHub to the stack
Before applying, check which frameworks the job listing emphasizes, whether PyTorch, TensorFlow, or JAX, and make sure your pinned repositories reflect that stack. A portfolio aligned to the team's toolchain signals you can contribute from day one.
Apply early to roles that fit
Migrate Mate lists ml software engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Distinguish research from production experience
Many ML engineer job listings separate model-building skills from deployment and serving experience. If you've shipped models to production, call that out explicitly in a dedicated bullet rather than burying it under a research project description.
Prepare a system design answer for ML pipelines
Most ML software engineer loops include at least one ML system design round covering feature stores, training pipelines, or online inference. Walk through data flow, latency requirements, and failure modes out loud so interviewers can see your architectural thinking, not just your coding ability.
Negotiate scope before accepting an offer
Once you have an offer, ask whether the role owns model deployment or hands off to a platform team. That distinction affects your day-to-day work significantly. Clarifying scope before you accept helps you evaluate fit beyond the title and compensation package.
ML Software Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most ml software engineers?
The companies hiring the most ml software engineers right now include Apple, Meta, and Amazon Web Services, with the largest share of openings in California, Washington, and Texas, based on current listings on Migrate Mate as of August 2026. Demand is concentrated at technology companies, financial institutions, and healthcare platforms scaling their AI infrastructure.
How many ml software engineer jobs are remote?
About 54% of ml software engineer openings are fully remote or hybrid as of August 2026, making it one of the more flexible engineering disciplines. Roles focused on NLP research, MLOps tooling, and model evaluation tend to have the highest share of remote options, while positions tied to robotics or on-device inference typically require on-site presence.
How do you become a ml software engineer?
Start by building a foundation in Python, linear algebra, and statistics, then work through core ML concepts using hands-on projects rather than coursework alone. Develop production-facing skills in model deployment, monitoring, and pipeline orchestration, since most roles expect more than research ability. A portfolio of shipped projects, even personal ones, carries significant weight with hiring teams.
Can you get hired as a ml software engineer with little or no experience?
You can break in without industry experience by building a focused portfolio that demonstrates end-to-end work: a model trained on real data, deployed to an endpoint, and monitored over time. Contributing to open-source ML projects, publishing reproducible experiments, and targeting companies with structured early-career programs all improve your chances without requiring years of prior employment.
What does the ml software engineer interview process look like?
Most loops include a recruiter screen, a take-home or live coding round covering data manipulation and model implementation, an ML system design session where you architect a pipeline end-to-end, and a behavioral round. Some companies add a research presentation or a debugging exercise on a broken training run. Loops typically run over one to three weeks.
Where can I find and apply to ml software engineer jobs?
You can find and apply to ml software engineer jobs on Migrate Mate, which lists current openings from companies across the United States. Search the listings to find roles that match your experience and specialization, then apply directly to each one that fits.
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