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Remote Rf Optimization Engineer Jobs in Missouri

Jobgether

$88K - $106K/yr

Our partner is looking for a Machine Learning Engineer - Inference Optimization based in ... Flexible remote work environment. * Opportunity to contribute to the growth of an innovative AI ...

Jobgether

Collaborate with engineering and editorial teams to implement technical improvements and scalable ... Remote-friendly working environment with flexibility and autonomy. * Chance to shape SEO practices ...

Jobgether

You will partner closely with marketing, product, engineering, and analytics teams to turn search ... Fully remote working environment with flexibility to work from your preferred location. * 30 ...

Navtech, Inc.

Teradata DBA

Saint Louis, MO · On-site +1

Teradata DBA Location: St Louis, MO - Remote Duration: 4+ Months Contract Qualifications and ... Optimizing developer, as well as, customer SQL. Design, model, configure, test, and install ...

Kemper

Senior Data Engineer

Saint Louis, MO · Remote

$103K - $140K/yr

... PA, Remote-RI, Remote-VA, St. Louis, Missouri Details Kemper is one of the nation's leading ... Warehouse sizing, performance tuning, and cost optimization * Implement scalable batch, micro-batch ...

S2Tech

Mainframe Developer (Remote)

Chesterfield, MO · Remote

$48.50 - $62.25/hr

Mainframe Developer Location: Remote About Us : Known for "Delighting the Client" through ... Work with mainframe systems and associated technologies to ensure optimal performance and ...

S2Tech

Mainframe Developer (Remote)

Chesterfield, MO · On-site +1

$48.50 - $62.25/hr

Mainframe Developer Location: Remote About Us : Known for "Delighting the Client" through ... Work with mainframe systems and associated technologies to ensure optimal performance and ...

Jobgether

You will collaborate with a remote-first team of experienced engineers while solving complex ... Improve application performance by identifying inefficient processing patterns and optimizing data ...

Jobgether

Knowledge of GPU scheduling, CUDA/ROCm optimization, multi-tenant inference systems, and ... Fully remote working environment with flexibility to work from Romania. * Chance to build ...

Jobgether

Distill large foundation models into smaller, faster, and more efficient models optimized for ... Remote-friendly work environment with an async-first culture. * Opportunity to solve challenging AI ...

Jobgether

$170K - $200K/yr

The position combines deep frontend engineering with visual innovation, performance optimization ... Comfortable working in a remote, international environment with distributed teams. Benefits:

nimble

Data Engineer

Chesterfield, MO · On-site +1

$113K - $136K/yr

Description Data Engineer Chesterfield Office Hybrid or Remote Why You'll Want to Join! Join a ... cycle optimization and clinical outcomes. Why This Role Matters Healthcare data engineering is ...

ClickHouse

$104K - $139K/yr

... optimization to distributed coordination and extension development. You'll shape critical ... remote

Jobgether

$128K - $148K/yr

... and optimizing engineering performance metrics. * Product leadership experience with a "maker ... Fully remote work opportunity with the flexibility to work from anywhere. * Home office allowance ...

Kohl's

Execute project tasks for systems and applications, focusing on optimizing efficiency and ... Engineer automated reconciliation frameworks and entitlement schemas to manage complex vendor ...

Jobgether

Collaborate with developers to implement event tracking, improve measurement infrastructure, and ... Remote-first work environment with flexibility. * Comprehensive medical, dental, vision, disability ...

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Remote Rf Optimization Engineer information

What is the difference between Remote Rf Optimization Engineer vs Remote Wireless Network Engineer?

AspectRemote Rf Optimization Engineer

The Remote Rf Optimization Engineer focuses on optimizing radio frequency performance for wireless networks, primarily working on signal quality, interference reduction, and network efficiency. The Remote Wireless Network Engineer also works on wireless systems but has a broader scope, including network design, deployment, and troubleshooting of entire wireless infrastructures. Both roles require knowledge of RF principles and certifications like CWNP, but the Optimization Engineer emphasizes fine-tuning existing networks, while the Network Engineer handles overall network setup and maintenance.

What is a Remote RF Optimization Engineer?

A Remote RF Optimization Engineer is a telecommunications professional who specializes in analyzing, optimizing, and improving the performance of wireless radio frequency (RF) networks from a remote location. Their main tasks include monitoring network KPIs, troubleshooting interference or coverage issues, and implementing solutions to enhance signal quality and capacity. Working remotely, they use specialized software tools to access, analyze, and optimize cellular networks such as LTE, 5G, or Wi-Fi, ensuring reliable communication services for users.

What are the key skills and qualifications needed to thrive as a Remote RF Optimization Engineer, and why are they important?

To thrive as a Remote RF Optimization Engineer, you need a solid background in wireless communication principles, network optimization, and a degree in electrical or telecommunications engineering. Familiarity with RF planning tools (such as Atoll, Actix, or TEMS), drive test equipment, and certifications like CCNA or relevant vendor-specific credentials are highly valued. Strong analytical thinking, problem-solving abilities, and effective remote communication skills set top performers apart in this role. These skills ensure optimal network performance, efficient troubleshooting, and seamless collaboration on distributed engineering teams.

What are some common challenges faced by Remote RF Optimization Engineers, and how can they be addressed?

Remote RF Optimization Engineers often encounter challenges such as limited on-site access, coordinating with field teams, and troubleshooting network issues without direct physical observation. These challenges can be addressed by leveraging advanced remote monitoring tools, maintaining clear communication channels with local technicians, and utilizing simulation software to analyze and resolve signal problems. Building strong relationships with cross-functional teams and staying updated on the latest industry best practices also help in effectively managing remote optimization tasks.
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What job categories do people searching Remote Rf Optimization Engineer jobs in Missouri look for? The top searched job categories for Remote Rf Optimization Engineer jobs in Missouri are:
What cities in Missouri are hiring for Remote Rf Optimization Engineer jobs? Cities in Missouri with the most Remote Rf Optimization Engineer job openings:
Remote Rf Optimization Engineer jobs near you

Machine Learning Engineer - Inference Optimization

Jobgether

On-site, Remote

$88K - $106K/yr

Full-time

Posted 4 days ago

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer - Inference Optimization based in Netherlands.

This role offers the opportunity to optimize the performance of advanced machine learning systems used in real-world production environments.
You will work at the intersection of research and engineering, transforming cutting-edge models into fast, reliable, and cost-efficient solutions.
Your work will directly impact model scalability, user experience, and the efficiency of AI-powered products.
You will dive deep into performance optimization, from model architecture and GPU execution to large-scale inference infrastructure.
Working with talented research, infrastructure, and product teams, you will help push the boundaries of what AI systems can achieve.
This position is ideal for an engineer who enjoys solving complex technical challenges and building high-performance ML systems from the ground up.

Accountabilities

As a Machine Learning Engineer specializing in inference optimization, you will own the performance and scalability of machine learning models in production. You will combine deep ML expertise, systems engineering, and performance analysis to deliver faster, more efficient AI experiences.

  • Optimize machine learning inference systems to improve latency, throughput, scalability, and operational cost.
  • Profile and identify bottlenecks across GPU and CPU inference pipelines, including memory usage, kernels, batching strategies, and data flow.
  • Implement advanced optimization techniques such as quantization, KV-cache optimization, speculative decoding, batching, streaming, and model simplification.
  • Collaborate with research engineers to productionize new model architectures and translate experimental results into reliable systems.
  • Build, improve, and maintain inference-serving infrastructure using modern frameworks, custom runtimes, or specialized serving solutions.
  • Benchmark model performance across different hardware environments, including GPUs, CPUs, and cloud-based systems.
  • Improve system reliability, monitoring, observability, and cost efficiency under real production workloads.
  • Contribute to engineering practices that improve the quality, scalability, and maintainability of ML infrastructure.
Requirements:

The ideal candidate is a technically strong machine learning engineer with experience optimizing production inference systems and a passion for high-performance AI engineering. You should enjoy working on complex technical problems, experimenting with new approaches, and taking ownership of critical systems.

  • Strong professional experience in ML inference optimization, high-performance machine learning systems, or related areas.
  • Deep understanding of machine learning fundamentals, including neural network architectures, attention mechanisms, memory optimization, and compute graphs.
  • Hands-on experience with PyTorch or similar deep learning frameworks and deploying models into production environments.
  • Experience with GPU performance optimization, including technologies such as CUDA, ROCm, Triton, or kernel-level tuning.
  • Proven experience scaling inference systems for real users beyond research prototypes or benchmarks.
  • Strong programming skills and the ability to work across machine learning and systems engineering domains.
  • Ability to operate effectively in fast-paced environments with ownership, autonomy, and evolving priorities.
  • Experience with inference frameworks such as TensorRT, ONNX Runtime, vLLM, or Triton is a plus.
  • Familiarity with large language models, long-context inference, distributed systems, low-latency services, or hardware optimization is considered an advantage.
  • Contributions to open-source ML systems or inference tooling are a plus.
Benefits:
  • Competitive compensation package with meaningful equity participation.
  • Opportunity to work on performance-critical AI systems with direct product impact.
  • High level of ownership over infrastructure that shapes scalability and efficiency.
  • Close collaboration with research, infrastructure, and product teams.
  • Opportunity to work on advanced machine learning technologies and real-world AI applications.
  • Engineering-focused culture that values technical excellence, experimentation, and quality.
  • Flexible remote work environment.
  • Opportunity to contribute to the growth of an innovative AI-focused organization.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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