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Rust Programming Jobs in Pennsylvania (NOW HIRING)

The Pennsylvania State University

Proficiency in C++ and C with strong object-oriented programming principles * High-performance ... Additional software languages (Rust, Bash, JavaScript, HTML, CSS) * Software libraries (Google ...

The Pennsylvania State University

Proficiency in C++ and C with strong object-oriented programming principles * High-performance ... Additional software languages (Rust, Bash, JavaScript, HTML, CSS) * Software libraries (Google ...

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$44.6K

$71K

$99.2K

How much do rust programming jobs pay per year?

As of Jul 24, 2026, the average yearly pay for rust programming in Pennsylvania is $71,027.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,100.00 and $88,700.00 per year, depending on experience, location, and employer.

What engineer makes $500,000 a year?

Senior software engineers, especially those working in high-demand fields like cloud computing, AI, or at major tech companies, can earn $500,000 or more annually. Achieving this level often requires extensive experience, specialized skills, and sometimes stock options or bonuses.

How much do Rust coders make?

Rust programmers typically earn between $80,000 and $130,000 annually, depending on experience, location, and industry. Skilled developers with knowledge of systems programming, concurrency, and performance optimization are in high demand and may command higher salaries.

What are some typical challenges faced by professionals working in Rust Programming roles?

Professionals in Rust Programming roles often encounter challenges such as mastering the language's strict ownership and borrowing rules, which require a deep understanding of memory safety concepts. Additionally, working with complex, performance-critical systems demands close attention to detail and rigorous testing practices. Collaboration with team members, especially when integrating Rust code with projects in other languages or legacy systems, also presents opportunities for problem-solving. Over time, these challenges help developers build robust, scalable solutions and deepen their expertise in cutting-edge systems programming.

Are Rust programmers in demand?

Rust programmers are increasingly in demand due to the language's focus on safety, performance, and concurrency, especially in systems programming, embedded systems, and WebAssembly development. Companies in technology, finance, and gaming are actively hiring Rust developers, and the demand is expected to grow as more organizations adopt Rust for critical applications.

What are the key skills and qualifications needed to thrive in the Rust Programming position, and why are they important?

Excelling in Rust Programming requires strong programming skills in Rust, understanding of systems programming concepts, and experience with memory management and concurrency. Familiarity with version control tools like Git, issue tracking systems, CI/CD pipelines, and optionally, certifications such as the Rust Programming Certificate, are often valuable. Problem-solving ability, collaboration, and clear communication are essential soft skills for success in team-based development environments. These skills and qualifications are vital because they ensure code safety, performance, and contribute to efficient, high-quality software development.

What is the salary of Rust programmer?

The salary of a Rust programmer varies based on experience, location, and industry, but typically ranges from $80,000 to $150,000 annually in many regions. Skilled developers with expertise in systems programming and concurrent programming tools like Rust are often in high demand and command higher salaries.

What is a Rust Programming job?

A Rust Programming job involves designing, developing, and maintaining software applications using the Rust programming language. Rust is known for its performance, memory safety, and concurrency features, making it popular for systems programming, web services, and embedded systems. Developers in this role typically work on optimizing performance, ensuring code safety, and integrating Rust with other technologies. Experience with Rust’s ownership model, concurrency features, and ecosystem tools like Cargo is often required.

What are the most commonly searched types of Rust Programming jobs in Pennsylvania? The most popular types of Rust Programming jobs in Pennsylvania are:
What are popular job titles related to Rust Programming jobs in Pennsylvania? For Rust Programming jobs in Pennsylvania, the most frequently searched job titles are:
What job categories do people searching Rust Programming jobs in Pennsylvania look for? The top searched job categories for Rust Programming jobs in Pennsylvania are:
What cities in Pennsylvania are hiring for Rust Programming jobs? Cities in Pennsylvania with the most Rust Programming job openings:
Rust Programming jobs near you
Infographic showing various Rust Programming job openings in Pennsylvania as of July 2026, with employment types broken down into 79% Full Time, 16% Part Time, 1% Temporary, 3% Contract, and 1% Nights. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $71,027 per year, or $34.1 per hour.

Machine Learning Engineer (Inference Optimization)

DEEPREC.AI

Philadelphia, PA • On-site

$250K - $450K/yr

Full-time

Posted 20 days ago

Job description

Machine Learning Engineer - Inference Optimization
Overview
We are looking for a Machine Learning Engineer focused on low-latency inference optimization to help build, tune, and productionize high-performance model serving systems. This role sits at the intersection of machine learning, systems engineering, and GPU performance. You will work on inference workloads where latency, throughput, reliability, and hardware efficiency all matter, and where a deep understanding of modern inference runtimes can meaningfully improve production outcomes.
You will work closely with researchers and engineers to understand model structure, identify inference bottlenecks, and turn research ideas into efficient production systems. The work may involve other types of models, but focuses on transformer-style architectures and structured inference workloads. You will evaluate and tune frameworks and related serving or compilation systems, while also reasoning about GPU execution, memory layout, batching strategies, precision tradeoffs, and end-to-end latency.
What you'll do:
  • Design, build, and optimize low-latency inference systems for production machine learning workloads.
  • Profile model inference pipelines across model execution, runtime configuration, batching, memory movement, serialization, networking, and I/O.
  • Evaluate, integrate, and tune inference runtime systems.
  • Improve latency, throughput, and GPU utilization for production inference workloads.
  • Build and support benchmarking and profiling tools to compare model variants, hardware targets, runtime configurations, and deployment strategies.
  • Debug performance issues involving GPU memory, compute saturation, kernel behavior, CPU/GPU coordination, data movement, and serving-layer overhead.
  • Help shape model and system design choices so that research models are efficient to deploy under real latency constraints.
  • Where necessary, collaborate with lower-level systems or GPU specialists on custom operators, kernel-level optimization, or hardware-specific performance work.
What we're looking for:
  • Experience deploying, optimizing, or operating machine learning inference workloads in production or production-like environments.
  • Programming experience in Python, Java, C# etc. and at least one systems language such as C, C , Rust, or Go.
  • Solid understanding of modern ML frameworks such as PyTorch, including model execution, export, tracing, compilation, and performance profiling.
  • Ability to reason about latency, throughput, batching, memory use, GPU utilization, and reliability under real workloads.
  • Strong practical judgment around tradeoffs between model quality, latency, throughput, implementation complexity, and maintainability.
Preferred qualifications:
  • Experience optimizing inference for latency-sensitive or high-throughput applications.
  • Experience with model optimization techniques such as quantization, pruning, distillation, operator fusion, graph lowering, custom operators, or model compilation.
  • Exposure to CUDA, Triton language, ROCm, PTX, CuTe, CUTLASS, FlashInfer, or similar low-level GPU programming tools.
  • Experience running inference workloads on Kubernetes or GPU clusters, including scheduling, autoscaling, observability, and resource management.
  • Background in mathematics, physics, computer science, engineering, statistics, or another technical field.
  • Demonstrated ability to improve real-world inference performance beyond a baseline framework implementation.

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