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Remote Wireless Research Engineer Jobs (NOW HIRING)

The Pennsylvania State University

Approval of remote and hybrid work is not guaranteed regardless of work location.For additional ... POSITION SPECIFICS We are seeking an experienced Research and Development Engineer to manage ...

DTEX Systems Inc

Threat Intel Research Engineer

$140K - $180K/yr

DTEX is seeking a highly skilled and mission-driven Threat Intel Research Engineer to join our ... Flexibility - Work in a hybrid or remote environment that balances collaboration with autonomy.

Jobgether

$158K/yr

Fully remote work environment with global collaboration opportunities. * Exposure to large-scale AI ... Collaboration with experienced researchers, engineers, and AI specialists. * Professional growth ...

Sumo Logic

Staff Threat Research Engineer

Incident responder or detection engineer roles * Demonstrated ability to progress threat research ... Prior experience in customer-facing technical roles (consulting, remote support, or advisory)

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Remote Wireless Research Engineer information

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How much do remote wireless research engineer jobs pay per year?

As of Jul 2, 2026, the average yearly pay for remote wireless research engineer in the United States is $106,012.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,000.00 and $104,000.00 per year, depending on experience, location, and employer.

What does a Remote Wireless Research Engineer do?

A Remote Wireless Research Engineer is responsible for studying, designing, and developing new wireless communication technologies, often while working remotely. They analyze wireless signal protocols, optimize network performance, and contribute to advancements in areas such as 5G, IoT, or Wi-Fi. Their work may include running simulations, developing prototypes, and collaborating with other engineers to solve complex technical problems. These engineers play a key role in improving the reliability, speed, and security of wireless networks, enabling better connectivity for devices and users.

What are some common challenges faced by a Remote Wireless Research Engineer, and how can they be addressed?

One common challenge for Remote Wireless Research Engineers is ensuring effective collaboration with multidisciplinary teams while working remotely. Since much of the research involves coordination with hardware, software, and network specialists, clear communication and proactive updates are essential. Additionally, staying current with rapidly evolving wireless technologies and standards can be demanding, so regularly participating in virtual conferences and training is beneficial. Adopting collaborative tools and establishing routine check-ins helps maintain alignment and drive successful project outcomes.

What is the difference between Remote Wireless Research Engineer vs Wireless Systems Engineer?

AspectRemote Wireless Research EngineerWireless Systems Engineer
Required CredentialsBachelor's or Master's in Electrical Engineering, Computer Science; knowledge of wireless protocolsBachelor's or Master's in Electrical Engineering, Computer Science; expertise in wireless systems
Work EnvironmentResearch labs, remote collaboration, R&D teamsDesign, testing, and deployment in labs or field
Employer & Industry UsageTech companies, research institutions, telecom firmsTelecom providers, hardware manufacturers, network providers
Search & Comparison IntentResearch, innovation, wireless protocol developmentSystem design, deployment, network optimization

The Remote Wireless Research Engineer focuses on developing new wireless technologies and protocols in research settings, often working remotely. In contrast, the Wireless Systems Engineer applies wireless knowledge to design, implement, and optimize wireless networks and systems. Both roles require similar educational backgrounds and industry experience but differ in their primary focus—research versus practical deployment.

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

To excel as a Remote Wireless Research Engineer, a strong background in wireless communication principles, signal processing, and a degree in electrical engineering or a related field are essential. Familiarity with technical tools such as MATLAB, Python, wireless simulation platforms, and knowledge of protocols like 5G or Wi-Fi, as well as relevant certifications (e.g., CWNA), are typically required. Strong problem-solving abilities, self-motivation, and effective remote communication skills distinguish top performers in this role. These competencies are vital for advancing wireless technology solutions, conducting independent research, and collaborating efficiently with distributed teams.
What cities are hiring for Remote Wireless Research Engineer jobs? Cities with the most Remote Wireless Research Engineer job openings:
What are the most commonly searched types of Wireless Research Engineer jobs? The most popular types of Wireless Research Engineer jobs are:
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Remote Wireless Research Engineer jobs near you

Research Engineer, Frontier Capabilities

Lila Sciences

Cambridge, MA • On-site, Remote

Other

Posted 23 days ago

Job description

Your Impact at LILA

The AI Research team is tackling one of the most exciting, open problems in AI: training LLMs to run long-horizon scientific discovery tasks. Our approach spans the full post-training stack - from SFT to asynchronous RL on agentic harnesses - teaching models to plan, use tools, and learn from experience in domains where the ground truth isn't a preference label, but a scientific result.

We're rapidly growing our Research Engineering org and seeking talented engineers and ML practitioners across levels to design, build, and optimize systems to push this frontier: scaling post-training, sharpening reasoning, and unlocking compute-intensive agentic-harness training. This is a rare chance to join an early team with the autonomy, flexibility, and compute to tackle frontier science problems.

We operate with high agency, and a bias toward execution. Below are several focus areas within the team. We ask that candidates select the stream that best matches their experience and excitement.

Work Streams

Stream A: GPU Optimization & Training Performance

Maximize hardware utilization across 100B+ parameter asynchronous RL training runs. Responsibilities include profiling, performance optimization, custom kernel development, communication-computation overlap, and long-context throughput improvements. You set and maintain the performance baseline.

Stream B: Stack & Infrastructure

Own the post-training infrastructure end-to-end - supervised fine-tuning, asynchronous RL with tool integration, and data pipelines. Build modular, reproducible workflows with single-command execution. Manage upstream framework upgrades and deliver composable pipelines spanning Data, SFT, and RL stages. You work tightly with Research Scientists to develop and productionize novel algorithms to run at scale.

Stream C: Model Experimentation

Bring deep, hands-on experience training large language models. Lead experimentation on reasoning model development, including mixture-of-experts stabilization, curriculum design, and synthetic reasoning trace generation. You have a bias toward experimental design and tracking, and know how to prioritize runs that yield promising outcomes.

Stream D: Evaluations & Benchmarks

Design and build best-in-class scientific agentic benchmarks and harnesses, along with the dashboards and leaderboards that inform every training decision. You have experience working with well known public benchmarks and have spent time building bespoke agentic benchmarks and harnesses.

Stream E: Agentic Capabilities & Frontier Research

Train models capable of planning, exploration, and tool use over extended horizons. Advance the state of the art in RL at scale with tool-calling, subgoal decomposition, and shared memory/skills across trials to expand the frontier of scientific agent capabilities.

What You'll Need to Succeed

  • Strong software engineering skills in Python; C++/CUDA a plus
  • Experience with distributed ML training frameworks (Megatron-LM, TorchTitan, DeepSpeed, Ray)
  • Understanding of large-scale model training techniques for 100B+ models
  • Experience with cloud or HPC environment
  • Ability to communicate technical results to internal and external stakeholders

Bonus Points For

  • Prior work with large scale scientific datasets or domain-specific modeling
  • Contributions to open-source ML frameworks
  • Experience with RL post-training (RLHF, GRPO, tool-augmented RL)
  • Experience training MoE architectures

Location

San Francisco, CA or Cambridge, MA (Remote, Hybrid, and On-Site available depending on team needs).

Apply