C++ AI Jobs

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Develop low-level drivers for the sensors or actuators of robots, develop the OS and middleware of the robots, integrate embedded algorithms such as Guidance, Navigation, Control, and Computer Vision, optimize runtime of algorithms on various hardware accelerators like GPU, TPU, DSp, develop the backbone of a command and control system for massive data ingestion and processing, develop a web-based front-end to display theatre of operations and allow mission conduction, build internal tools to improve efficiency and reduce technical debt, develop connectors between existing company tools like ERP, MES, PLM, implement code into production-ready environments, ensure seamless integration with Harmattan AI’s systems, conduct rigorous code reviews, test algorithms in real-world environments, develop monitoring tools, track model performance and continuously improve deployed solutions, collaborate closely with other hardware and software teams to align development with system requirements, and communicate findings effectively to stakeholders.

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Design, develop, and iterate on data-driven ASR models for streaming and non-streaming conversational speech applications; research and implement state-of-the-art end-to-end speech recognition architectures tailored to the medical domain; train, evaluate, and optimize ASR models across accuracy, latency, and resource utilization dimensions; preprocess and curate large-scale speech datasets to support robust model training; collaborate closely with LLM, product, and clinical teams to integrate speech technologies into the broader Hippocratic AI platform; contribute to the team's research culture through experimentation, documentation, and knowledge sharing.

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Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.

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The responsibilities include translating business requirements into requirements for AI/ML models, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms especially transformers, testing and evaluating the AI/ML/DL models, publishing the models, datasets, and evaluations, deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines for models with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.

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Translate business requirements into AI/ML model requirements. Prepare data to train and evaluate AI/ML/DL models. Build AI/ML/DL models using state-of-the-art algorithms, especially transformers, sometimes leveraging existing algorithms from research. Test and evaluate models, benchmark quality, and publish models, datasets, and evaluations. Deploy models in production by containerizing them. Work with customers and internal employees to refine model quality. Establish continuous learning pipelines for models with online or transfer learning. Build and deploy containerized applications on cloud or on-premise environments.

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Develop and maintain scalable, reproducible workflows for ingesting and processing large volumes of point cloud, imagery, and geospatial data. Convert datasets from various sensor providers into Mach9's standardized internal formats. Build CI/CD pipelines and automated checks that guarantee the correctness and consistency of data pipelines, including regression detection on dataset processing. Optimize processing performance, query speed, and storage efficiency across large geospatial datasets. Work closely with the customer success team to efficiently resolve issues and unblock customer projects. Build and maintain agentic harness for automated dataset triage and code patching, automatically proposing or applying fixes and escalating when human judgment is needed. Collaborate with ML and product teams to make data readily usable for training, inference, and visualization. Work closely with customers and data-provider partners to facilitate data integration, including occasional travel. Engage with data formats that have sparse or missing documentation to solve puzzles related to those data formats.

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Design, build, and operate backend systems that power image generation and image editing experiences in ChatGPT. Develop scalable APIs, services, and infrastructure that support multimodal AI workflows. Optimize reliability, latency, throughput, and cost across large-scale distributed systems. Partner with researchers to productionize new image generation capabilities and bring them to users quickly and safely. Collaborate closely with Android, iOS, web, and full-stack engineers to build seamless end-to-end product experiences. Drive technical architecture decisions across storage, serving, orchestration, and platform systems. Use data and experimentation to identify opportunities for improving user experience, performance, and system efficiency. Help shape engineering culture through technical leadership, mentorship, and operational excellence.

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The role involves developing and integrating autonomy software solutions onto unmanned platforms across multiple domains including air, maritime, space, or effects/expendables, encompassing payload computer bring-up, container-based deployment, and configuration across onboard compute, sensors, and command-and-control interfaces. The engineer leads a small team through design, development, and delivery of major capabilities or sub-programs, acts as the primary technical interface with related teams and external partners, authors and negotiates interface contracts, mentors mid-level engineers, drives design reviews, post-mortems, and documentation rigor, and owns pre-deployment build, configuration, and validation processes. The job includes travel to test sites to support live mission operations, hardware/software debugging in lab and field environments, capturing and analyzing mission data for continuous improvement, building tools and processes for integration and test efficiencies, managing C2 interoperability and standards compliance, and participating in hiring and onboarding. Travel commitments approximate 10-20% annually.

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Develop and integrate autonomy software solutions onto unmanned platforms including payload computer bring-up, container-based deployment, and configuration across onboard compute, sensors, and command-and-control interfaces. Collaborate closely with the Autonomy Capabilities team, Perception team, feature crews, and external partners such as platform integrators, vehicle/autopilot control vendors, and C2 providers to deliver and validate mission-critical functionality. Own the build, configuration, and validation process for mission-ready systems and coordinate compatibility, mission readiness, and release cadence with capability and feature teams. Travel to test sites to support live mission operations including safety checks, system bring-up, and troubleshooting under time-critical constraints. Diagnose and resolve integration issues across autonomy stacks, payload computers, and embedded systems in lab and field environments, including memory, CPU, and timing profiling under operational loads. Capture mission and test data, reproduce issues in simulation, and work with autonomy capability owners to implement fixes. Build tools and processes to improve integration timelines, test reliability, and team efficiency. Implement and validate compliance with command-and-control standards and coordinate with C2 providers on interface contracts and integration milestones. Travel approximately 10-20% of the year to office locations, customer sites, and integration/test events.

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The Technical Lead Manager will own datasets throughout the training infrastructure and set the direction for how training jobs read data, including APIs, storage contracts, versioning model, benchmarks, debugging tools, and reliability guarantees to make data access consistent across current and future training frameworks. Responsibilities include designing and building a unified dataset read platform for multiple training frameworks; defining dataset APIs, storage-format expectations, registration/versioning, and migration paths to ensure reproducible and maintainable data access; building reliability into the read path such as stateful iteration, caching, fast restart, recovery, and clear operational contracts; developing terminal and web-based visualizers to inspect data late in the pipeline; writing and reviewing production code in core data loading, service, caching, and reliability paths; and partnering with teams working on training frameworks, reinforcement learning, multimodal models, storage, runtime, and cluster infrastructure. Over time, the role will expand to owning broader data movement systems including checkpoint loads/saves and snapshot transfers, working closely with technical leads and infrastructure teams.