Description

<div class="content-intro">Based in Mountain View, CA., NeuroPace is a commercial-stage medical device company focused on transforming the lives of people suffering from epilepsy by reducing or eliminating the occurrence of debilitating seizures. Its novel and differential RNS System is the first and only commercially available, brain-responsive platform that delivers personalized, real-time treatment at the seizure source. At NeuroPace, employees are our greatest asset. We are continually searching for solution-oriented individuals who can bring energy and creativity to our growing workforce. At NeuroPace, our success depends upon our ability to recruit and retain the most talented, enthusiastic and dedicated people we can find and providing them with a dynamic and challenging environment in which to thrive.</div>We are currently seeking an&nbsp;AI Research Engineer&nbsp;to join our team to develop multimodal LLMs, reasoning agents, and large‑scale RAG systems. This individual&nbsp;will directly help people living with neurological disease;&nbsp;you’ll&nbsp;be part of a fast-moving group dedicated to&nbsp;using AI tools to accelerate&nbsp;impact&nbsp;of an FDA-approved implantable device.&nbsp;You’ll&nbsp;own model training pipelines, high‑throughput data and indexing systems, and retrieval‑augmented generation powering production applications. Ideal candidates have experience training large&nbsp;multimodal&nbsp;models, building scalable data/feature stores, and shipping agentic systems with strong observability and safety. Ideal candidates will be Bay&nbsp; Key Responsibilities Model Training &amp; Inference&nbsp; <ul> <li>Train/fine‑tune LLMs and multimodal models (text/vision/audio; bonus: neural/ECoG).&nbsp;</li> <li>Use SFT, DPO/ORPO, RLHF/RLAIF, and parameter‑efficient tuning (LoRA/QLoRA).&nbsp;</li> <li>Build agentic systems (tool use, planning, memory, reasoning) with attention to latency, safety, and cost.&nbsp;</li> <li>Optimize&nbsp;inference: quantization, graph/tensor optimizations, speculative decoding, distillation, caching.&nbsp;</li> </ul> Data &amp; Indexing&nbsp; <ul> <li>Build fast pipelines for&nbsp;ingest,&nbsp;feature extraction, multimodal alignment, and dataset curation.&nbsp;</li> <li>Design billion‑scale indexing (vector, hybrid&nbsp;sparse+dense, graph‑based).&nbsp;</li> <li>Implement observability: lineage, drift, coverage, bias, differential performance, cost.&nbsp;</li> </ul> RAG &amp; Knowledge Systems&nbsp; <ul> <li>Build production RAG (chunking, hybrid search, reranking, context optimization).&nbsp;</li> <li>Maintain continuous refresh pipelines (incremental indexing, TTL, schema evolution).&nbsp;</li> <li>Integrate ontologies/knowledge graphs for grounding and attribution.&nbsp;</li> </ul> Evaluation, Safety &amp; Reliability&nbsp; <ul> <li>Develop structured evaluations for faithfulness, factuality, consistency,&nbsp;and&nbsp;multimodal alignment.&nbsp;</li> <li>Implement guardrails: prompt hardening, policy filters, PII/PHI handling, output verification.&nbsp;</li> <li>Run A/B tests; define metrics for quality, latency, and cost.&nbsp;</li> </ul> Platform &amp; Collaboration&nbsp; <ul> <li data-leveltext="" data-font="Symbol" data-listid="13" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-aria-posinset="1" data-aria-level="1">Partner with infra for distributed training (FSDP/ZeRO, multi‑GPU/TPU), registries, and ML CI/CD.&nbsp;</li> <li data-leveltext="" data-font="Symbol" data-listid="13" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-aria-posinset="2" data-aria-level="1">Mentor engineers and contribute to long‑term technical strategy.&nbsp;</li> </ul> Requirements Minimum Qualifications&nbsp; <ul> <li>2+ years in ML/AI (or equivalent impact).&nbsp;</li> <li>Master’s&nbsp;in CS or related field, or equivalent experience.&nbsp;</li> <li>Hands‑on LLM/multimodal training (PyTorch/TensorFlow/HuggingFace/SageMaker).&nbsp;</li> <li>Strong engineering skills: Python, TypeScript, APIs, testing, profiling, and observability.&nbsp;</li> </ul> Preferred Qualifications&nbsp; <ul> <li>4+ years in ML/AI and production ML system experience.&nbsp;</li> <li>Experience shipping agentic systems with planning, tool use, schemas, memory, and guardrails.&nbsp;</li> <li>Multimodal&nbsp;expertise: CLIP‑style contrastive models, BLIP/LLaVA/Flamingo, Whisper/NeMo, video, neural signals.&nbsp;</li> <li>Production RAG (chunking, grounding, hallucination mitigation, cost/latency tuning).&nbsp;</li> <li>Distributed training (FSDP/DeepSpeed/ZeRO), mixed precision, checkpointing.&nbsp;</li> <li>Information retrieval: query understanding, retrieval‑aware generation, knowledge graphs.&nbsp;</li> <li>Causal inference or counterfactual evaluation for scientific/biomedical ML.&nbsp;</li> </ul> $135K-$175K Base - Compensation will be determined based on several factors, including but not limited to skill set, years of experience, and geographic location. NeuroPace is proud to be an equal opportunity employer and values the contributions of our culturally diverse workforce.San Francisco and Los Angeles applicants: The Company will consider for employment qualified applicants with Criminal Histories in a manner consistent with the requirements of the Los Angeles Fair Chance in Hiring Ordinance or the San Francisco Fair Chance Ordinance (as applicable) <div class="content-conclusion">Benefits <ul> <li>Medical, Dental &amp; Vision Insurance</li> <li>Voluntary Life</li> <li>401K</li> <li>RSU</li> <li>529 plan</li> <li>ESPP Program</li> <li>Health &amp; Wellness Program</li> <li>Generous Paid Time Off plus eleven paid holidays</li> <li>FSA &amp; Commuter Benefits</li> </ul> NeuroPace is proud to be an equal opportunity employer and values the contributions of our culturally diverse workforce. San Francisco and Los Angeles applicants: The Company will consider for employment qualified applicants with Criminal Histories in a manner consistent with the requirements of the Los Angeles Fair Chance in Hiring Ordinance or the San Francisco Fair Chance Ordinance (as applicable) PRIVACY NOTICE:&nbsp; NeuroPace takes its responsibility to protect your personal information seriously, and it uses reasonable safeguards to avoid unauthorized use or disclosure of it, and inadvertent loss or impermissible alteration of it.&nbsp; NeuroPace complies with all applicable federal and state laws and regulations that govern the handling of your personal information.&nbsp; If you would like more detailed information on NeuroPace’s privacy policies, please refer to neuropace.com/privacy/ for reference.&nbsp; NeuroPace retains candidate resumes and applications in its files for future reference and/or consideration for other available job postings.&nbsp; If you do not wish for your resume and applications materials to be retained in NeuroPace files, or wish to obtain a listing of any personal information that NeuroPace has stored about you, please contact us at <a href="mailto:privacy@neuropace.com">privacy@neuropace.com</a>.&nbsp; &nbsp; &nbsp;</div>

AI Research Engineer – Generative AI

Description

NeuroPace

Pipeline