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Remote Machine Learning Compiler Engineer Jobs in Pittsburgh, PA

Cmu

AI Security Software Engineer

Pittsburgh, PA · On-site +1

Develop machine learning-based prototypes, tools, and systems for AI security applications ... Apply software engineering best practices to build scalable, maintainable systems, grounded design ...

Mastech Digital

AI Engineer

Pittsburgh, PA · Remote

$70 - $76/hr

Remote - US, Canada, India Salary: $70.00-$76.00/Hourly Role: AI Engineer Primary Skills: Python ... machine learning. Required Qualifications: - Bachelor's or Master's degree in Computer Science ...

Deloitte

AI and Data Science Engineer III

Pittsburgh, PA · On-site +1

$111K - $133K/yr

Deliver governed datasets and feature engineering and serving patterns for machine learning training and real-time inference, including online and offline consistency, caching, latency targets, and ...

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All JobsRemote Machine Learning Compiler Engineer JobsRemote Machine Learning Compiler Engineer Jobs in Pittsburgh, PA

Remote Machine Learning Compiler Engineer information

See Pittsburgh, PA salary details

$72.8K

$162.6K

$199K

How much do remote machine learning compiler engineer jobs pay per year?

As of Jun 19, 2026, the average yearly pay for remote machine learning compiler engineer in Pittsburgh, PA is $162,551.00, according to ZipRecruiter salary data. Most workers in this role earn between $138,800.00 and $199,000.00 per year, depending on experience, location, and employer.

How does a Remote Machine Learning Compiler Engineer typically collaborate with cross-functional teams to optimize model deployment?

As a Remote Machine Learning Compiler Engineer, you will frequently collaborate with data scientists, hardware engineers, and software developers to ensure that machine learning models are efficiently compiled and deployed on target platforms. Communication often takes place through virtual meetings, code reviews, and shared documentation tools. You'll be responsible for translating research models into optimized code, troubleshooting performance bottlenecks, and integrating feedback from various stakeholders. Effective teamwork is crucial, as the success of deployments often depends on iterative feedback and close alignment with both the ML research and hardware teams.

What is a Remote Machine Learning Compiler Engineer?

A Remote Machine Learning Compiler Engineer is a software engineer who specializes in developing and optimizing compilers specifically for machine learning workloads, while working from a remote location. Their primary responsibilities include designing and implementing compiler features that translate machine learning models into efficient code for various hardware platforms, such as CPUs, GPUs, or specialized accelerators. They collaborate closely with machine learning researchers, hardware engineers, and software developers to ensure high performance and compatibility. In addition to strong programming skills, they typically require expertise in compiler theory, machine learning frameworks, and hardware architectures. This role allows for flexible, location-independent work while contributing to cutting-edge AI technologies.

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

AspectRemote Machine Learning Compiler EngineerRemote Data Scientist
Required CredentialsBachelor's or Master's in Computer Science, Software Engineering, or related fields; knowledge of compiler design and ML frameworksBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in programming, statistics, and data analysis
Work EnvironmentPrimarily software development, compiler optimization, and ML model deploymentData analysis, model building, and interpretation of results
Industry UsageTech companies, AI startups, hardware firms focusing on ML hardware accelerationTech, finance, healthcare, and research organizations

While both roles involve working with machine learning, the Remote Machine Learning Compiler Engineer focuses on developing and optimizing compilers for ML models, whereas the Remote Data Scientist concentrates on analyzing data and building predictive models. The roles share some technical skills but differ in their core responsibilities and work environments.

What are the key skills and qualifications needed to thrive as a Remote Machine Learning Compiler Engineer, and why are they important?

To thrive as a Remote Machine Learning Compiler Engineer, you need a strong background in computer science, proficiency in programming languages like C++ and Python, and expertise in compiler theory and machine learning frameworks. Familiarity with ML compilers such as TVM or XLA, and experience using version control and CI/CD systems are commonly required, along with a relevant bachelor's or master's degree. Outstanding problem-solving, collaboration, and communication skills are essential for working effectively in distributed teams and across technical domains. These skills and qualities enable the development of efficient, scalable ML solutions that bridge software and hardware, ensuring high performance and innovation.
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Remote Machine Learning Compiler Engineer jobs near you

Senior Software Engineer, Machine Learning Inference Platform

Stack AV

Pittsburgh, PA • On-site, Remote

Full-time

Posted 4 days ago

Job description

About Stack:
Stack is developing revolutionary AI and advanced autonomous systems designed to enhance safety, reliability, and efficiency of modern operations. Stack's autonomous technology incorporates cutting-edge advancements in artificial intelligence, robotics, machine learning, and cloud technologies, empowering us to create innovative solutions that address the needs and challenges of the dynamic trucking transportation industry. With decades of experience creating and deploying real world systems for demanding environments, the Stack team is dedicated to developing an autonomous solution ecosystem tailored to the trucking industry's unique demands.
About the Role:
In the Senior Engineer role, you will own meaningful subsystems of Stack AV's inference platform and drive them from design through production. You will be the go-to engineer for one or more areas such as model onboarding, serving APIs, metering, observability, performance optimization, or tenant isolation. The role requires strong hands-on implementation, production debugging, thoughtful design, and the ability to mentor engineers while keeping delivery moving.
Responsibilities:
  • Own technical design and delivery of subsystems in a high-throughput, low-latency inference platform capable of handling multi-tenant, enterprise-grade inference workloads.
  • Develop robust API layers (gRPC, WebSockets, REST, etc.) and developer SDKs that abstract complex distributed inference orchestration into seamless, reliable token streams.
  • Build and harden a multi-tenant control plane to enable accurate metering, rate limiting, quotas, tenant isolation and noisy-neighbor fairness across the platform.
  • Optimize inference performance across the entire system stack, including the model engine layer.
  • Build observability and SLOs to gain insights into system economics, cache-hit rates, GPU utilization and cost accounting per model and per tenant.
  • Partner with product and infrastructure teams on model onboarding, capacity planning, external API contracts and customer adoption.
  • Decompose ambiguous work, drive issues to closure, and raise the engineering bar through code quality, reviews, testing, and mentoring.

Qualifications:
  • Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
  • Experience: 4+ years of experience building and operating backend distributed systems end to end.
  • Strong Data & ML systems fundamentals: data-intensive distributed systems, concurrency, networking and performance profiling.
  • Hands-on experience with large-scale inference services on GPUs, including KV caches, prefill/decode stages and throughput/latency trade-offs.
  • Direct experience with inference engines (TensorRT, vLLM, etc) or serving frameworks (Dynamo, Triton or equivalent).
  • Technical Skills:
    • Strong programming skills in C++, Go, Rust or Python.
    • Familiarity with deep learning frameworks (PyTorch, etc.) as well as model parallelism.
    • Familiarity with GPU computing primitives such as CUDA, NCCL, NVLink, and hardware-specific optimizations.
    • Practical understanding of high-performance networking architectures, including InfiniBand, RoCE, and low-latency cluster communication.
  • Problem-Solving: Strong analytical and problem-solving skills.
  • Autonomous vehicles (AV) experience is a bonus.

We are proud to be an equal opportunity workplace. We believe that diverse teams produce the best ideas and outcomes. We are committed to building a culture of inclusion, entrepreneurship, and innovation across gender, race, age, sexual orientation, religion, disability, and identity.
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Please Note: Pursuant to its business activities and use of technology, Stack AV complies with all applicable U.S. national security laws, regulations, and administrative requirements, which can restrict Stack AV's ability to employ certain persons in certain positions pursuant to a range of national security-related requirements. As such, this position may be contingent upon Stack AV verifying a candidate's residence, U.S. person status, and/or citizenship status. This position may also involve working with software and technologies subject to U.S. export control regulations. Under these regulations, it may be necessary for Stack AV to obtain a U.S. government export license prior to releasing its technologies to certain persons. If Stack AV determines that a candidate's residence, U.S. person status, and/or citizenship status will require a license, prohibit the candidate from working in this position, or otherwise be subject to national security-related restrictions, Stack AV expressly reserves the right to either consider the candidate for a different position that is not subject to such restrictions, on whatever terms and conditions Stack AV shall establish in its sole discretion, or, in the alternative, decline to move forward with the candidate's application.

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