Machine Learning AI Jobs

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Conduct research on state-of-the-art Computer Vision methodologies and participate in the creation and curation of training and validation datasets. Perform statistical analyses and develop visualization tools to ensure data quality. Build and refine training pipelines and metrics to enhance model performance. Develop and optimize Computer Vision algorithms for multiple robotics/aerospace projects. Implement ML/CV models into production-ready environments, ensure seamless integration with Harmattan AI's systems, and conduct rigorous code reviews. Test algorithms in real-world environments and develop monitoring tools to track model performance and continuously improve deployed solutions. Work closely with software and simulation teams to align development with system requirements and communicate findings effectively to stakeholders.

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The member of the technical staff will be responsible for collaborating with Modal’s SDK team and other product engineers to build out a framework and process for agent productivity evaluation. They will treat developer experience optimization as a scientific problem by defining quantitative objectives, designing systems to measure performance, and translating results into product improvements. They are also expected to stay on top of new developments in tools and workflows, and to work with customers to understand how they are using coding agents with Modal and where additional value can be provided.

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Senior AI Engineers are responsible for researching, building, optimizing, and deploying the production machine learning (ML) systems that thousands of developers integrate with their systems. Their work focuses on solving complex research and engineering problems to build the engine for the next generation of AI-driven software, particularly in the area of speech modeling including Speech-to-Text (STT) and Text-to-Speech (TTS).

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Senior AI Engineers at Inworld are responsible for researching, building, optimizing, and deploying production machine learning systems that support thousands of developers. Their work focuses on overcoming research and engineering challenges related to speech modeling, including speech-to-text and text-to-speech systems, addressing complex problems such as data collection, training infrastructure, reinforcement learning alignment environments, and ultra-low latency inference optimizations for AI-driven software.

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Conduct original research to advance the state of the art in machine learning and artificial intelligence. Design, implement, and evaluate novel algorithms, models, or training approaches at large scale. Collaborate with researchers and engineers to translate research insights into production systems and real-world applications.

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As an ML Research Scientist at Eight Sleep, responsibilities include using AI and Machine Learning to transform sensor data into personalized intelligent health and fitness experiences. You will work closely with a cross-functional R&D and production team to prototype and ship solutions that improve sleep and health. Specific projects involve advancing the Pod's adaptive thermoregulation system using reinforcement learning and closed-loop control, developing multimodal health foundation models integrating physiology and environmental context from various data sources, and building high-fidelity physiological simulators to model the impact of daily behaviors on sleep and readiness. The role requires applying machine learning techniques to health-related problems and data to deliver impactful products for users.

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You will be part of a computer vision team responsible for building computer vision models for object recognition and tracking, video understanding, scene matching, and related tasks. Your role includes developing computer vision models and pipelines that leverage and extend the latest state-of-the-art methods and architectures. You will design experiments and conduct benchmarks to evaluate and improve model performance in real-world scenarios. Additionally, you will apply and develop techniques to adapt models to target hardware and constraints associated with downstream ML/AI tasks. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.

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The Audio Engineer will own and scale audio quality across voice AI products, ensuring voices sound great to human listeners across thousands of voices and recording conditions. Responsibilities include identifying and correcting audio artifacts, loudness inconsistencies, frequency imbalances, and sibilance issues in large-scale voice datasets; designing and implementing scalable audio processing pipelines including EQ, compression, de-essing, dynamic range optimization, and normalization strategies; optimizing audio quality across real and synthetic voices for consistent product experience; leading audio quality decisions during on-site voice actor recording sessions such as microphone selection, placement, gain staging, and environment setup; defining, documenting, and enforcing audio quality standards for external vendors to meet training and product needs; converting manual audio workflows into automated, repeatable, code-based systems; collaborating with research to improve training data quality, especially TTS speaker-specific fine-tuning; and contributing to synthetic data pipelines by defining and validating acoustic characteristics and guiding sound profile production and evaluation.

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As Technical Director, you will lead the development and delivery of advanced AI solutions, ensuring technical excellence across the business unit focused on Public Services. You will provide hands-on technical guidance and expertise for complex, high-priority client projects, advise on solution architecture, advanced machine learning modelling, and engineering best practices. You will define and champion the technical strategy and vision for Public Services clients, lead recruiting, structuring, and professional development for technical staff within the business unit, act as the senior technical authority in key client meetings to secure and expand market opportunities, and create and share impactful technical thought leadership through conferences, articles, and other media.

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As an Electrical Engineer - Systems, you will work on Machine Learning/AI hardware systems projects to develop solutions for current and future data center deployments. Your responsibilities include working with the hardware team on test vehicle and bring up board design, evaluating end-to-end system design trade-offs. You will lead EE circuit level design and collaborate with power, thermal, and mechanical teams to drive AI hardware system design. The role requires working with product teams to ensure system goals are met and collaborating with ASIC/FPGA, Software, and Verification teams for proper verification of features. You will also engage with manufacturing teams to ensure designs are manufacturable and ready for volume production, support field teams for deployed systems, gather system requirements, define architecture, execute hardware design, product validation, lead system bring up, validation, NPI, deployment, and sustaining of hardware solutions. Cross-functional collaboration with Hardware, Software, Mechanical, Thermal, Validation, Manufacturing, and external vendors is essential, as is driving system development from concept through production, and leading debug and root cause analysis of deployed systems.