This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As an Applied Research Engineer at Snorkel AI, you will own the infrastructure that powers our model training and evaluation work. This is a hands-on role where you will build and operate GPU cluster infrastructure, training pipelines, and the tooling that allows our research and engineering teams to run experiments reliably and at scale. You will work closely with research scientists and engineers, translating training requirements into robust, reproducible systems—and proactively removing infrastructure blockers before they slow down the work that matters most. Snorkel AI operates in a fast-paced, high-impact environment. We are looking for someone who takes pride in operational excellence, loves solving complex distributed systems problems, and thrives when given real ownership. Location: Redwood City or San Francisco — OR REMOTE Main Responsibilities - Set up and manage GPU cluster infrastructure on major cloud providers (e.g., AWS HyperPod) for distributed model training, including networking, provisioning, and cost tracking. - Build and operate job orchestration and scheduling systems (e.g., Kubernetes, Slurm, or cloud-native equivalents) to reliably launch and manage training, rollout, and evaluation jobs across multi-node clusters. - Integrate and maintain ML training frameworks and post-training pipelines, ensuring they run stably and reproducibly at scale. - Set up and maintain experiment tracking, dataset versioning, and model artifact management to support fast iteration. - Monitor and optimize cluster health, inter-node communication, and resource utilization; implement fault tolerance and auto-recovery so long-running jobs survive node failures. - Work closely with research scientists and ML engineers to understand requirements, unblock experiments, and evolve infrastructure as our training workloads needs change. Qualifications - Hands-on experience managing GPU clusters on major cloud providers, including provisioning, network configuration, and cost management. - Experience with distributed compute orchestration tools such as Kubernetes, Slurm, or equivalent cluster management systems. - Working knowledge of distributed training concepts: parallelism strategies, memory optimization techniques, and inter-node communication. - Experience with setting up, managing, and integrating ML experiment tracking and data/model versioning tools. - Strong Python proficiency and solid software engineering fundamentals such as version control, modular design, and automation. - Ability to work in a fast-moving, iterative environment and take end-to-end ownership of ambiguous infrastructure problems. - Hands-on experience with post-training workflows such as supervised fine-tuning (SFT) or reinforcement learning (RLHF, GRPO, or similar) is a strong plus, but not required. Salary Range $150,000.00 – $180,000.00 Benefits Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Our client is an innovative product company that transforms how businesses access and utilize data. Using over 1 million global IPs and a database of more than 650 million verified profiles, they empower companies to make data-driven decisions with precision and ease. They are looking for creative, driven individuals eager to push boundaries and contribute to a rapidly expanding client base. If you’re passionate about data science, analytics, or tech, this is your chance to join a dynamic team that delivers impactful, real-time data solutions. Their collaborative, innovative culture provides the perfect environment for those looking to grow alongside industry experts. Qualifications - 2+ years of experience with web scraping and parsing techniques. - Proven experience working at a large scale (tens or hundreds of millions of requests per day). - Strong hands-on experience with Kubernetes and Docker. - Solid experience with browser-based scraping, using tools such as Puppeteer or Selenium. - Strong programming skills, particularly in languages such as Python or NodeJS. - Familiarity with website technologies such as HTML, AJAX, and JavaScript. - Strong analytical and problem-solving skills. - Level of spoken English: at least an upper-intermediate. Requirements - Experience in reverse engineering JavaScript challenges is a significant plus. - Experience with network scanners and sniffers such as Wireshark. - Experience with web development, API design, or ETL processes. - Familiarity with data storage technologies such as SQL or NoSQL databases. - Experience in full-stack development. - Experience with machine learning and AI techniques for detecting and defending against bots. Benefits - People-first management with minimal bureaucracy. - A friendly company culture, proven by employees who choose to return. - Flexible working hours. - 29 days of PTO (18 working days per year plus all national holidays). - 9 paid recovery days. - Full financial and legal support for independent contractors. - Free English classes, with native speakers or Ukrainian teachers. - Dedicated HR support.

• Architecting the Research Pipeline: Building the infrastructure to reproduce and adapt SOTA papers within 4–6 week cycles, ensuring our models outpace the global fraud landscape. • Engineering Physics-Based Defenses: Developing "attack-agnostic" features—leveraging optical flow, specular reflection, and 3D geometry—to exploit immutable physical laws that attackers cannot easily circumvent. • Solving for Domain Heterogeneity: Implementing advanced Domain Adaptation and Generalization techniques to eliminate performance variance across device types (iOS vs. Android), demographics, and environmental conditions. • Optimizing for SaaS Scale: Writing high-performance, maintainable Python code to ensure complex CV models remain cost-efficient and latency-optimized. • Strategic Prototyping: Working from first principles to decide when a novel architecture is required versus when the solution lies in superior data curation or procedural attack generation.

About Centific Centific is a frontier AI data foundry that curates diverse, high-quality data, using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe, scalable AI deployment. Our team includes more than 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem—comprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 markets—to create contextual, multilingual, pre-trained datasets; fine-tuned, industry-specific LLMs; and RAG pipelines supported by vector databases. Our zero-distance innovation™ solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster. Our mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers. We aim to help these organizations unlock significant business value by deploying GenAI at scale, helping to ensure they stay at the forefront of technological advancement and maintain a competitive edge in their respective markets. About Job PhD Research Intern — Speech AI Centific AI Research Fulltime - 40 hours per week Summary Centific AI Research seeks a PhD Research Intern to design and evaluate speech‑first models, with a focus on Spoken Language Models (SLMs) that reason over audio and interact conversationally. You’ll move ideas from prototype to practical demos, working with scientists and engineers to deliver measurable impact. Scope of Work - End‑to‑end speech dialogue systems (speech‑in/speech‑out) and speech‑aware LLMs. - Alignment between speech encoders and text backbones via lightweight adapters. - Efficient speech tokenization and temporal compression suitable for long‑form audio. - Reliable evaluation across recognition, understanding, and generation tasks—including robustness and safety. - Latency‑aware inference for streaming and real‑time user experiences. Example Projects - Prototype a conversational SLM using an SSL speech encoder and a compact adapter on an existing LLM; compare against strong baselines. - Create a data recipe that blends conversational speech with instruction‑following corpora; run targeted ablations and report findings. - Build an evaluation harness that covers ASR/ST/SLU and speech QA, including streaming metrics (latency, stability, endpointing). - Ship a minimal demo with streaming inference and logging; document setup, metrics, and reliability checks. - Author a crisp internal write‑up: goals, design choices, results, and next steps for productionization. Minimum Qualifications - PhD candidate in CS/EE (or related) with research in speech, audio ML, or multimodal LMs. - Fluency in Python and PyTorch, with hands‑on GPU training; familiarity with torchaudio or librosa. - Working knowledge of modern sequence models (Transformers or SSMs) and training best practices. - Depth in at least one area: (a) discrete speech tokens/temporal compression, (b) modality alignment to LLMs via adapters, or (c) post‑training/instruction tuning for speech tasks. - Strong experimentation habits: clean code, ablations, reproducibility, and clear reporting. Preferred Qualifications - Experience with speech generation (neural codecs/vocoders) or hybrid text+speech decoding. - Background in multilingual or code‑switching speech and domain adaptation. - Hands‑on work evaluating safety, bias, hallucination, or spoofing risks in speech systems. - Distributed training/serving (FSDP/DeepSpeed), and experience with ESPnet, SpeechBrain, or NVIDIA NeMo. Tech Stack - PyTorch, CUDA, torchaudio/librosa; experiment tracking (e.g., Weights & Biases). - LLM backbones with lightweight adapters; neural audio codecs and vocoders as needed. - FastAPI/gRPC for services; ONNX/TensorRT and quantization for efficient inference. What We Offer - Competitive stipend and hands-on projects with measurable real-world impact. - Mentorship from applied scientists and engineers; opportunities to publish and present. - Access to modern GPU infrastructure and a supportive environment for fast, responsible experimentation. - Flexible location and schedule options, subject to team needs. Benefits: - Comprehensive healthcare, dental, and vision coverage - 401k plan - Paid time off (PTO) - And more! Learn more about us at centific.com. Hourly Rate : $40/hr Centific is an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, citizenship status, age, mental or physical disability, medical condition, sex (including pregnancy), gender identity or expression, sexual orientation, marital status, familial status, veteran status, or any other characteristic protected by applicable law. We consider qualified applicants regardless of criminal histories, consistent with legal requirements.