Post-train, fine-tune, and distil frontier language models using RL, distillation, and reward design in Python. Build production-grade simulation systems that model thousands of individuals in real-time for a Societal World Model at a YC-backed startup.

You’ll work with Python, FastAPI, TypeScript, React, and GCP. Electric Twin builds a behavioral simulation platform that creates “digital twins” of real populations, helping organizations test decisions with 95% accuracy.

You will work with Python, PyTorch or JAX, Docker, CI/CD, and cloud platforms to deploy ML models for protein design. The team builds scalable infrastructure for training, inference, and evaluation across molecular and protein datasets, taking research prototypes into production.

You will work with agentic LLMs, reinforcement learning, Python, PyTorch, JAX, HF, and tools like CleanRL/AutoGen/LangGraph on an agentic code generation tool. The team designs multi-agent and RL approaches, builds research prototypes, creates evaluation suites, and curates datasets from complex technical documents.

EXL Service★3.7 (2k)· London

You will work with Generative AI tools (OpenAI, LangChain, Hugging Face, Vertex AI, Bedrock), LLMs, vector databases (FAISS, Pinecone, ChromaDB), and Python frameworks (Transformers, PyTorch, scikit-learn). As part of EXL’s Data Science Team, you’ll build and deploy GenAI solutions for enterprise use cases such as customer service, document automation, summarization, and knowledge retrieval.

Sonar★5.0 (2)· London

You will work with Python, core ML frameworks, and post-training techniques including GRPO, PPO, RLVR, SFT, PEFT, and preference/safety alignment (Rust or SonarQube flagship languages are a plus). You will join a cross-disciplinary team developing advanced products that post-train models to power enterprise agentic coding practices, ensuring code meets enterprise standards.

You'll work with Python, Go, React, TypeScript, Docker, Kubernetes, APIs, model-serving systems and distributed-inference workloads. Partnering directly with customers, you'll architect, build and deploy production AI/ML systems on Lightning's end-to-end platform, covering inference infrastructure, distributed systems and AI workflows.

You'll work with LLMs, chain-of-thought prompting, RAG architectures, multi-agent tool use, Python, and FastAPI. Electric Twin builds synthetic populations for behavioral prediction, using LLMs to simulate human responses for global brands. As part of the Science team, you'll design memory systems and decision-making frameworks for realistic AI agents.

You will use Python, core ML frameworks, and post-training techniques (GRPO, PPO, RLVR, SFT, PEFT) to develop enterprise-grade coding agents. This cross-disciplinary team combines Sonar’s static analysis with LLM post-training to help customers build agents generating high-quality code meeting enterprise standards.

You will work with Generative AI, LLMs, OpenAI, LangChain, Hugging Face, PyTorch, TensorFlow, FastAPI, Streamlit, and cloud platforms (AWS/GCP/Azure). You will design and deploy GenAI solutions—including RAG pipelines, fine-tuning, and prompt engineering—for enterprise use cases such as customer service, document automation, and knowledge retrieval on the Data Science Team at a global data and AI company.

Deloitte★3.9 (14k)· London

Work with Python, CI/CD, PyTorch, MLFlow, FastAPI, Azure (Fabric, Data Factory, ML Studio, Synapse, Cosmos), and LLM tools (RAG, embeddings). You will lead end-to-end AI/ML projects for Deloitte’s Tax & Legal Data and AI Studio, delivering analytics and AI-driven client solutions.

You'll work with Python and modern ML tooling, building and fine-tuning specialized AI agents, production data pipelines, and owning the full AI/ML stack from model selection to inference infrastructure. Prior Foundry develops simulations and LLMs for social science and public policy, building agents for the public sector. Founding role, no team size stated.

You'll use Python, SQL, LangChain, XGBoost, PyTorch, Hugging Face Transformers, Airflow, MLOps tooling, vector databases, and cloud services (AWS/GCP/Azure), building classical ML and LLM/RAG systems. Qogita is a B2B wholesale marketplace; the Data Science team builds the intelligence layer driving its platform.

Train and fine-tune Vision-Language Models (VLMs) and multimodal foundation models using Python and PyTorch or JAX, implementing research from arXiv into production code and owning the full model lifecycle. The company builds large-scale models for autonomous physical-world capabilities, aiming to protect democracies via technological leadership.

Sonar★5.0 (2)· London

Sonar seeks a senior researcher with an advanced degree and 4+ years of ML industry experience. The role involves developing enterprise-grade coding agents using Python, ML frameworks, and post-training techniques (e.g., RLVR, SFT) for Sonar’s AI code verification platform. You will work within a cross-disciplinary team to translate prototypes into products.

Work with Rust, Python, Java, Kotlin, C++ and related languages, building a next-generation software platform that integrates robotics, sensors and autonomous systems for demanding environments in the security sector.

Working with Python, PyTorch, SGLang, LLMs, embeddings, reranking models, PEFT/LoRA, RAG, Git, Docker, and microservices, you will build and deploy AI-powered products for specialised financial markets. Joining a small, experienced engineering team, you'll collaborate directly with founders and domain experts to solve technical problems with significant autonomy.

Work with LLMs, prompt engineering, fine-tuning, RAG, and vector databases (FAISS, Pinecone, ChromaDB) using Python, LangChain, Transformers, PyTorch, TensorFlow, and cloud platforms. As part of the Data Science team, you will design, develop, and deploy enterprise-grade GenAI solutions for customer service, document automation, summarization, and knowledge retrieval.

Design and deploy enterprise-scale GenAI solutions using LLMs, RAG, LangChain, OpenAI, Hugging Face, vector databases, and cloud platforms (AWS/GCP/Azure). Develop LLM-powered applications, build RAG pipelines, and fine-tune models with Python, PyTorch/TensorFlow, FastAPI, and Streamlit.

You'll work with PyTorch, JAX, TensorFlow, NCCL, MPI, Slurm, Kubernetes, and Ray. Join a cross-functional team building core tools for high-volume data processing, RL agent training, and large-scale foundation models to protect democracies. You'll extend deep learning frameworks, scale distributed training, and debug production ML pipelines.

Senior AI/ML Research Engineer role building enterprise-scale AI, GenAI, and RAG solutions on Azure for a global energy Super Major client. You'll use Python, Kubernetes, Databricks, Spark/PySpark, SQL/NoSQL, CI/CD, and LangChain, plus Azure OpenAI, Terraform, and Kafka. You'll lead end-to-end ML delivery and design AI agent architectures.

Optimize ML inference for edge accelerators and GPUs using TensorRT, CUDA, Qualcomm QNN, Triton, OpenCL, MLIR, ONNX, Python, and C++. You will run large transformer-based models efficiently on low-power edge devices to enable Wayve’s first driving product.

You will use Python, Trio, PyTorch, TensorFlow, JAX, Kubernetes, Rust, C++, and GPU clusters. The team advances reinforcement learning for large language models, improving Claude’s autonomy, coding, and reasoning capabilities.

Design and develop low-latency, high-throughput trading and market data systems using modern C++ (C++17/20) on Linux, with additional exposure to Python, Rust, Go, Java, or C#. The team handles strategy execution, market data processing, and core infrastructure on the direct trading path.

You'll use Python, machine learning, reinforcement learning, LLMs, and Kubernetes to run experiments like AI Debate and jailbreak evaluations. As part of Anthropic's Alignment Science team, you'll conduct exploratory AI safety research on risks from powerful future systems, covering AI control and alignment stress-testing.