Our core product suite includes: ● Glia Voice Agents - multimodal conversational agents capable ... remote patient monitoring, and specialty patient support programs (e.g., oncology, dementia ...
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Our core product suite includes: ● Glia Voice Agents - multimodal conversational agents capable ... remote patient monitoring, and specialty patient support programs (e.g., oncology, dementia ...
Remote Packet Core information
What are the key skills and qualifications needed to thrive as a Remote Packet Core Engineer, and why are they important?
To thrive as a Remote Packet Core Engineer, you need a strong background in telecommunications, IP networking, and mobile core technologies, typically supported by a degree in computer science, electrical engineering, or a related field. Familiarity with packet core platforms (such as EPC, 5GC), network protocols, virtualization tools, and relevant certifications like Cisco CCNP or Nokia NRS are commonly required. Analytical thinking, effective communication, and problem-solving skills help professionals excel when managing complex, distributed network environments. These skills are crucial for ensuring reliable mobile data connectivity, resolving technical issues promptly, and supporting the scalability of telecom networks.
What is the difference between Remote Packet Core vs Remote Network Engineer?
| Aspect | Remote Packet Core | Remote Network Engineer |
|---|---|---|
| Required Certifications | CCNA, CCNP, LTE/5G certifications | CCNA, CCNP, Cisco certifications |
| Work Environment | Telecommunications companies, mobile network providers | IT firms, telecom providers, network service companies |
| Industry Usage | Primarily in mobile and wireless networks | Broadly in enterprise and service provider networks |
| Common Search/Comparison | Yes | Yes |
Remote Packet Core specialists focus on designing, deploying, and maintaining core network elements for mobile networks, especially LTE and 5G. Remote Network Engineers have a broader role, managing various network infrastructure components across different environments. While both roles require networking certifications and involve remote work, Remote Packet Core professionals specialize in mobile core networks, making their skills more specific to telecom providers.
What are some common challenges faced by professionals working in a Remote Packet Core role, and how can they be addressed?
Professionals in a Remote Packet Core role often face challenges related to managing and troubleshooting complex network issues without direct physical access to hardware. Effective remote collaboration, strong documentation skills, and the use of advanced monitoring tools are essential to overcome these barriers. Additionally, staying updated on the latest network protocols and virtualization technologies can help address evolving technical requirements. Regular communication with cross-functional teams ensures alignment and swift resolution of critical incidents.
What is a Remote Packet Core?
A Remote Packet Core refers to the virtualized or cloud-based implementation of a mobile network's packet core functions, allowing operators to manage and deliver data services without relying on traditional, on-premises hardware. This architecture enables mobile network providers to centralize network management, increase scalability, and reduce operational costs by leveraging remote data centers or cloud platforms. It's especially important for supporting 4G LTE and 5G networks, as it allows faster deployment and improved flexibility for handling data traffic across multiple locations.
What are popular job titles related to Remote Packet Core jobs in Massachusetts? For Remote Packet Core jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Remote Packet Core jobs in Massachusetts look for? The top searched job categories for Remote Packet Core jobs in Massachusetts are:
What cities in Massachusetts are hiring for Remote Packet Core jobs? Cities in Massachusetts with the most Remote Packet Core job openings:
Full-time
Posted 6 days ago
Job description
OutcomesAI is a healthcare technology company building an AI-enabled nursing platform designed to augment clinical teams, automate routine workflows, and safely scale nursing capacity.
Our solution combines AI voice agents and licensed nurses to handle patient communication, symptom triage, remote monitoring, and post-acute care — reducing administrative burden and enabling clinicians to focus on direct patient care.
Our core product suite includes:
● Glia Voice Agents – multimodal conversational agents capable of answering patient calls, triaging symptoms using evidence-based protocols (e.g., Schmitt-Thompson), scheduling visits, and delivering education and follow-ups.
● Glia Productivity Agents – AI copilots for nurses that automate charting, scribing, and clinical decision support by integrating directly into EHR systems such as Epic and Athena.
● AI-Enabled Nursing Services – a hybrid care delivery model where AI and licensed nurses work together to deliver virtual triage, remote patient monitoring, and specialty patient support programs (e.g., oncology, dementia, dialysis).
Our AI infrastructure leverages multimodal foundation models — incorporating speech recognition (ASR), natural language understanding, and text-to-speech (TTS) — fine-tuned for healthcare environments to ensure safety, empathy, and clinical accuracy. All models operate within a HIPAA-compliant and SOC 2–certified framework. OutcomesAI partners with leading health systems and virtual care organizations to deploy and validate these capabilities at scale. Our goal is to create the world’s first AI + nurse hybrid workforce, improving access, safety, and efficiency across the continuum of care.
Lead the end-to-end technical development of speech models (ASR, TTS, Speech-LLM) — from architecture, training strategy, and evaluation to production deployment.You’ll act as an individual contributor and mentor, guiding a small team working on model training, synthetic data generation, active learning, and inference optimization for healthcare applications. As a Tech Lead specializing in ASR, TTS, and Speech LLM, you will spearhead the technical development of speech models. This involves everything from architectural design and training strategies to evaluation and production deployment.
This role is a blend of individual contribution and mentorship. You will guide a small team focused on model training, synthetic data generation, active learning, and inference optimization, all within the context of healthcare applications.
What You’ll Do- Own the technical roadmap for STT/TTS/Speech LLM model training: from model selection → fine-tuning → deployment.
- Evaluate and benchmark open-source models (Parakeet, Whisper, etc.) using internal test sets for WER, latency, and entity accuracy.
- Design and review data pipelines for synthetic and real data generation (text selection, speaker selection. voice synthesis, noise/distortion augmentation).
- Architect and optimize training recipes (LoRA/adapters, RNN-T, multi-objective CTC + MWER).
- Lead integration with Triton Inference Server (TensorRT/FP16) and ensure K8s autoscaling for 1000+ concurrent streams.
- Implement Language Model biasing APIs, WFST grammars, and context biasing for domain accuracy.
- Guide evaluation cycles, drift monitoring, and model switcher/failover strategies.
- Mentor engineers on data curation, fine-tuning, and model serving best practices.
- Collaborate with backend/ML-ops for production readiness, observability, and health metrics.
- Deep expertise in speech models (ASR, TTS, Speech LLM) and training frameworks (PyTorch, NeMo, ESPnet, Fairseq).
- Proven experience with streaming RNN-T / CTC architectures, LoRA/adapters, and TensorRT optimization.
- Telephony robustness: Codec augmentation (G.711 μ-law, Opus, packet loss/jitter), AGC/loudness norm, band-limit (300–3400 Hz), far-field/noise simulation.
- Strong understanding of telephony noise, codecs, and real-world audio variability.
- Experience in Speaker Diarization, turn detection model, smart voice activity detectionEvaluation: WER/latency curves, Entity-F1 (names/DOB/meds), confidence metrics.
- TTS : VITS/FastPitch/Glow-TTS/Grad-TTS/StyleTTS2, CosyVoice/NaturalSpeech-3 style transfer, BigVGAN/UnivNet vocoders, zero-shot cloning.
- Speech LLM: Model development and integration with Voice agent pipeline.
- Experience deploying models with Triton Inference Server, Kubernetes, and GPU scaling.
- Hands-on with evaluation metrics (WER, F1 on entities, latency p50/p95).
- Familiarity with LM biasing, WFST grammars, and context injection.
- Strong mentorship and code-review discipline.
- M.S. / Ph.D. in Computer Science, Speech Processing, or related field.
- 7–10 years of experience in applied ML, at least 3 in speech or multimodal AI.
- Track record of shipping production ASR/TTS models or inference systems at scale.
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.