ML Research Engineer Jobs

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As the Manager for the Sovereign AI Modelling team, you will manage a team of scientists and engineers, fostering a culture of high-performance, innovation, and continuous learning. You will stay up-to-date with the latest research in large language models (LLMs) and related fields, lead scalable strategies to train frontier models, and collaborate with cross-functional teams across modelling, forward-deployed engineering, and solutions architecture. The role is hands-on and research-driven, involving designing and implementing novel research ideas, shipping state-of-the-art models to production, and maintaining deep connections to academia and government. You will dive into the latest literature on LLMs, experiment with frontier models, and lead a team of talented engineers and researchers to build scalable, production-ready solutions.

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The role involves owning late-stage training decisions that shape model capabilities, including designing and iterating on high-quality data mixtures for late-stage and annealing training runs, developing methods for sourcing, filtering, and weighting data to enhance model performance, driving targeted improvements in coding, mathematics, and reasoning via curated data strategies and training interventions, developing and evaluating synthetic data pipelines for scalable training signal generation, researching and optimizing multi-stage learning rate schedules and compute allocation, implementing methods to extend effective context length without hurting short-context performance, building evaluations to distinguish real capability improvements from benchmark overfitting, and measuring how mid-training interventions scale with compute and data while developing new approaches when existing methods reach limits. The role crosses traditional pre-training and post-training boundaries and encompasses both research and engineering responsibilities.

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The Senior Machine Learning Scientist will train, evaluate, and iterate on ML models and agentic systems for customer feedback, including owning custom fine-tuning pipelines. They will run experiments end-to-end, track results rigorously, and make recommendations on what to ship, iterate, or retire. The role involves building and maintaining LLM-powered features such as retrieval pipelines, reranking systems, insight agents, data mining agents, and automated taxonomy generation. The scientist will design and run robust evaluation frameworks including building test sets, defining metrics, evaluating non-deterministic systems, handling class imbalance, and automating checkpoint comparisons. They will improve and extend semantic search and retrieval methods, write production-quality code, and collaborate closely with Engineering on productionisation, model serving, data pipelines, and monitoring. The role includes working with Product and Commercial teams to translate business needs into practical ML solutions and supporting client evaluations and accuracy benchmarking. Additionally, the scientist will mentor team members, review code and research, and integrate relevant advances from literature into the product.

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The Senior Applied AI Manager is responsible for owning the strategy and execution for AI science at Oumi. This includes setting the applied science agenda, building and leading the team, and being accountable for the science quality of every feature shipped on the platform. The role covers the full model development lifecycle, including data strategy, pre-training and post-training methodology, evaluation science, and production deployment, as well as developing agentic systems that automate and improve each stage. The manager works closely with the CEO and product leadership to translate company strategy into a concrete AI science roadmap and executes it with a team of ML engineers and applied researchers. Responsibilities include defining and driving the research and engineering roadmap, recruiting and managing a high-performing team, leading experimentation across the training stack, owning the data side of model development, designing evaluation frameworks and automated feedback loops, researching and developing agent-based systems for the training lifecycle, partnering with infrastructure and product teams to ensure reliable feature deployment, and contributing to open source and community collaborations.

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Design and implement evaluation pipelines to measure the performance and reliability of AI models, develop automated testing frameworks to assess model outputs at scale, analyze model performance using both traditional statistical metrics and AI-specific evaluation methods, evaluate AI systems built on modern architectures such as LLM-based applications and Retrieval-Augmented Generation (RAG), identify potential issues related to accuracy, hallucinations, bias, safety, and model drift, conduct adversarial testing to uncover vulnerabilities and ensure safe model behavior, collaborate with engineering and AI teams to improve prompt design, model outputs, and system performance, monitor model performance in production, and help define best practices for AI evaluation and observability.

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Lead a talented team of engineers focused on improving AKASA’s machine learning capabilities and delivering cutting-edge products. Supervise and directly contribute across all parts of the LLM stack, including model fine-tuning, inference, evaluation, and deployment. Develop infrastructure and tooling to improve the model development lifecycle. Oversee a high-performing team via hands-on contributions and coaching. Translate business requirements into technical designs that work within constraints such as latency, cost, performance, and uptime. Set the vision and direction for the team and attract top talent to join AKASA. Attend in-office co-working days every Wednesday as part of the local R&D team.

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Architect conversational agents that are stateful, context-aware, and capable of maintaining long-running coherent dialogues to handle complex reasoning tasks. Build retrieval-augmented generation (RAG) pipelines that ground large language model (LLM) responses in proprietary data to ensure high accuracy and minimize hallucinations. Lead the development of natural language processing (NLP) pipelines to extract structured insights from varied unstructured data sources, initially text and eventually audio. Implement advanced personalization layers that adapt model behavior and tone dynamically based on user history and context. Own the deployment lifecycle of LLM models including prompt architecture, evaluation frameworks, latency optimization, and cost management on Vertex AI. Provide technical mentorship by reviewing code, setting architectural standards, and guiding technical decision-making for ML engineers without people management responsibilities.

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As a Sovereign AI Intern, you will design, train and improve upon cutting-edge models to serve public interest, help develop new techniques to train and serve models safer, better, and faster, train extremely large-scale models on massive datasets, learn from experienced senior machine learning technical staff, and work closely with product teams to develop solutions.

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The Lead Machine Learning Engineer will own the development and improvement of the system predicting the next action salespeople should take to advance their relationships. Responsibilities include selecting the best model architecture and approach, involving a mixture of LLM steps and traditional ML models, picking evaluation metrics, designing systems to analyze models in production to identify areas for improvement, and identifying when to use the human data team for training or validation datasets. The engineer will read relevant research to find the best approach for their use case and, in partnership with the CTO, define how machine learning works with product engineering, model operations, and human data teams and how the team should develop moving forward.

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As an engineer in the Offline Driving Intelligence (ODIN) team at Zoox, the responsibilities include developing advanced multimodal large language models to enhance environmental understanding for robotaxis, designing model architectures and training techniques using sensor inputs and large scale data, driving end-to-end machine learning solutions from research to production using Zoox's data pipelines and infrastructure, collaborating with perception, planning, safety, and systems teams to integrate models into the vehicle's decision-making pipeline, and validating and optimizing solutions using real-world driving scenarios to contribute directly to the safety and reliability of Zoox's autonomous system.