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Enigma
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  • Machine Learning Researcher | Python | PyTorch | Machine Learning | De... Read More
    Machine Learning Researcher | Python | PyTorch | Machine Learning | Deep Learning | Hybrid, Seattle, WA Role Overview We are seeking Machine Learning Research Engineers / Scientists to join our team working on groundbreaking physics foundation models. The successful candidate will develop, train and deploy to production large-scale AI foundation models for weather and energy. What You'll Do Architect and implement innovative ML models for complex spatiotemporal data analysis. Lead end-to-end development of large-scale AI systems, from research to production. Drive the optimisation of training and inference pipelines for maximum performance. Conduct validation experiments and performance analysis. Spearhead long-term research initiatives with significant real-world impact. Collaborate with world-class researchers and engineers. We expect you to have Proven track record in developing and deploying deep learning models. Advanced proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, Jax). Demonstrated experience with distributed training systems and large-scale data pipelines. Strong software engineering practices and system design principles. Excellent problem-solving and analytical skills. Outstanding communication and collaboration abilities. Nice to have MSc or PhD in Artificial Intelligence, Computer Science, or related technical field. Published research in prestigious AI conferences/journals (NeurIPS, ICML, etc.). Hands-on experience with one or several of the following: transformers, diffusion models, self-supervised learning, foundation model training/fine-tuning. Join us in pushing the boundaries of physics foundation models! Machine Learning Researcher | Python | PyTorch | Machine Learning | Deep Learning | Hybrid, Seattle, WA Read Less
  • Machine Learning Researcher | Python | PyTorch | Machine Learning | De... Read More
    Machine Learning Researcher | Python | PyTorch | Machine Learning | Deep Learning | Hybrid, Seattle, WA Role Overview We are seeking Machine Learning Research Engineers / Scientists to join our team working on groundbreaking physics foundation models. The successful candidate will develop, train and deploy to production large-scale AI foundation models for weather and energy. What You'll Do Architect and implement innovative ML models for complex spatiotemporal data analysis. Lead end-to-end development of large-scale AI systems, from research to production. Drive the optimisation of training and inference pipelines for maximum performance. Conduct validation experiments and performance analysis. Spearhead long-term research initiatives with significant real-world impact. Collaborate with world-class researchers and engineers. We expect you to have Proven track record in developing and deploying deep learning models. Advanced proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, Jax). Demonstrated experience with distributed training systems and large-scale data pipelines. Strong software engineering practices and system design principles. Excellent problem-solving and analytical skills. Outstanding communication and collaboration abilities. Nice to have MSc or PhD in Artificial Intelligence, Computer Science, or related technical field. Published research in prestigious AI conferences/journals (NeurIPS, ICML, etc.). Hands-on experience with one or several of the following: transformers, diffusion models, self-supervised learning, foundation model training/fine-tuning. Join us in pushing the boundaries of physics foundation models! Machine Learning Researcher | Python | PyTorch | Machine Learning | Deep Learning | Hybrid, Seattle, WA Read Less
  • Machine Learning Engineer | Python | Pytorch | Distributed Training |... Read More
    Machine Learning Engineer | Python | Pytorch | Distributed Training | Optimisation | GPU | Hybrid, San Jose, CA Title: Machine Learning Engineer Location: San Jose, CA Responsibilities: Productize and optimize models from Research into reliable, performant, and cost-efficient services with clear SLOs (latency, availability, cost). Scale training across nodes/GPUs (DDP/FSDP/ZeRO, pipeline/tensor parallelism) and own throughput/time-to-train using profiling and optimization. Implement model-efficiency techniques (quantization, distillation, pruning, KV-cache, Flash Attention) for training and inference without materially degrading quality. Build and maintain model-serving systems (vLLM/Triton/TGI/ONNX/TensorRT/AITemplate) with batching, streaming, caching, and memory management. Integrate with vector/feature stores and data pipelines (FAISS/Milvus/Pinecone/pgvector; Parquet/Delta) as needed for production. Define and track performance and cost KPIs; run continuous improvement loops and capacity planning. Partner with ML Ops on CI/CD, telemetry/observability, model registries; partner with Scientists on reproducible handoffs and evaluations. Educational Qualifications: Bachelors in computer science, Electrical/Computer Engineering, or a related field required; Master’s preferred (or equivalent industry experience). Strong systems/ML engineering with exposure to distributed training and inference optimization. Industry Experience: 3–5 years in ML/AI engineering roles owning training and/or serving in production at scale. Demonstrated success delivering high-throughput, low-latency ML services with reliability and cost improvements. Experience collaborating across Research, Platform/Infra, Data, and Product functions. Technical Skills: Familiarity with deep learning frameworks: PyTorch (primary), TensorFlow. Exposure to large model training techniques (DDP, FSDP, ZeRO, pipeline/tensor parallelism); distributed training experience a plus Optimization: experience profiling and optimizing code execution and model inference: (PTQ/QAT/AWQ/GPTQ), pruning, distillation, KV-cache optimization, Flash Attention Scalable serving: autoscaling, load balancing, streaming, batching, caching; collaboration with platform engineers. Data Read Less
  • AI Research Scientist | Machine Learning | Deep Learning | Natural Lan... Read More
    AI Research Scientist | Machine Learning | Deep Learning | Natural Language Processing | LLM | Hybrid | San Jose, CA Title: AI Research Scientist Location: San Jose, CA Responsibilities: Design, execute, and analyze machine learning experiments, establishing strong baselines and selecting appropriate evaluation metrics. Stay up to date with the latest AI research; identify, adapt, and validate novel techniques for company-specific use cases. Define rigorous evaluation protocols, including offline metrics, user studies, and adversarial (red team) testing to ensure statistical soundness. Specify data and annotation requirements; develop annotation guidelines and oversee quality control processes. Collaborate closely with domain experts, product managers, and engineering teams to refine problem statements and operational constraints. Develop reusable research assets such as datasets, modular code components, evaluation suites, and comprehensive documentation. Work alongside ML Engineers to optimize training and inference pipelines, ensuring seamless integration into production systems. Contribute to academic publications and represent the company in research communities, as needed. Educational Qualifications: Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field is strongly preferred. Candidates with a master’s degree and exceptional research or industry experience will also be considered. Industry Experience: 3–5 years of experience in AI/ML research roles, ideally in applied or product-focused environments. Demonstrated success in delivering research-driven solutions that have been deployed in production. Experience collaborating in cross-functional teams across research, engineering, and product. Publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ACL, CVPR) are a plus. Technical Skills: Strong foundational knowledge in machine learning and deep learning algorithms. Hands-on experience with PEFT/LoRA, adapters, fine-tuning techniques, and RLHF/RLAIF (e.g., PPO, DPO, GRPO). Ability to read, implement, and adapt state-of-the-art research papers to real-world use cases. Proficiency in hypothesis-driven experimentation, ablation studies, and statistically sound evaluations. Advanced programming skills in Python (preferred), C++, or Java. Experience with deep learning frameworks such as PyTorch, Hugging Face, NumPy, etc. Strong mathematical foundations in probability, linear algebra, and calculus. Domain expertise in one or more areas: natural language processing (NLP), symbolic reasoning, speech processing, etc. Ability to translate research insights into roadmaps, technical specifications, and product improvements. AI Research Scientist | Machine Learning | Deep Learning | Natural Language Processing | LLM | Hybrid | San Jose, CA Read Less

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Astrid-Lindgren-Weg 12 38229 Salzgitter Germany