Applied ML Engineer Jobs

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Design and train state-of-the-art machine learning algorithms for semantic segmentation, object detection, and classification tailored to aerial imagery. Build high-level tactical features on top of base semantic data, such as real-time road vectorization, trafficability analysis, and dynamic obstacle mapping. Architect pipelines that temporally and spatially align semantic data from multiple moving UAVs into a cohesive Common Operational Picture (COP). Optimize and deploy these algorithms directly into the tactical C2 platform, utilizing quantization, pruning, and hardware acceleration to meet strict real-time compute constraints.

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Design, implement, and productionize advanced ML models and techniques such as federated learning, differential privacy, or synthetic data generation for data anonymization. Build and maintain the core backend infrastructure and APIs to securely process and serve anonymized data at Mercor's scale. Benchmark the anonymization pipeline against industry best practices and regulatory standards like k-anonymity, continuously running experiments to improve both privacy guarantees and data utility. Collaborate cross-functionally with Legal, Security, and Engineering teams to translate compliance requirements into robust, model-driven solutions. Act as the subject matter expert on data anonymization, balancing applied ML, complex data pipeline engineering, and driving architectural decisions for data privacy.

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As a Machine Learning Intern at Nomagic, you will dive into complex problems of physical manipulation to enhance robot capabilities. Your responsibilities include expanding the perception abilities of the robotic system to handle a wider variety of products, detecting anomalies such as identifying when a robot picks more than one item or when an item is disassembling, training models to solve multiple problems with various loss functions, and productionizing machine learning models which involves performance monitoring and A/B testing. You will work on developing groundbreaking technology and collaborate with top professionals in an English-speaking environment, with opportunities to play with robots daily and contribute directly to impactful results.

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Build and maintain data pipelines for large video generation models, including data ingestion, parsing, filtering, preprocessing, and dataset curation at scale, using tools such as AWS S3 and DynamoDB. Design and run annotation workflows across platforms such as MTurk, Prolific, and Mechanical Turk, including task design, quality control, and label validation. Train, evaluate, and improve smaller supporting models used for data filtering, quality assessment, preprocessing, or other parts of the ML pipeline. Partner closely with research and engineering teams to turn experimental workflows into scalable, repeatable systems that support model training and evaluation. Own data quality across the pipeline by identifying bottlenecks, failure modes, and low-quality sources, and continuously improving tooling and processes. Build internal tools and automation that make it easier to prepare datasets, launch annotation jobs, monitor outputs, and support model development end to end. Drive larger pipeline projects from start to finish, such as new dataset creation efforts or upgrades to labeling and preprocessing infrastructure. Work within a Kubernetes-based training infrastructure, ensuring datasets are properly prepared, formatted, and delivered to training clusters. Profile and optimize research model inference scripts used in preprocessing steps, ensuring that model-driven filtering and transformation stages run within practical time and cost constraints when applied to large-scale raw data.

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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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Act as the technical owner for enterprise customer vision-language model (VLM) post-training engagements. Translate customer requirements into concrete multimodal post-training specifications and workflows. Design and execute visual data generation, filtering, and quality assessment processes, including image-text pair curation, annotation pipelines, and synthetic data generation for visual tasks. Run supervised fine-tuning, preference alignment, and reinforcement learning workflows for vision-language models. Design task-specific evaluations for visual understanding, grounding, OCR, document parsing, and other multimodal capabilities. Interpret evaluation results and feed learnings back into core post-training pipelines.

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Act as the technical owner for enterprise customer post-training engagements involving audio and speech workloads, translating customer requirements into concrete post-training specifications for ASR, TTS, and speech-to-speech tasks; design and execute data generation, preprocessing, augmentation, and quality filtering processes for audio corpora; fine-tune and adapt audio/speech models for customer-specific use cases, owning delivery from requirements through deployment; design task-specific evaluations for audio model performance (noise robustness, speaker variation, latency) and interpret results; build reusable applied tooling and workflows that accelerate future customer engagements.

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Contributors may design original computational STEM problems simulating real scientific workflows, create computationally intensive problems requiring Python programming to solve, develop problems requiring non-trivial reasoning and creative problem-solving, verify solutions using Python with standard libraries such as Numpy, Pandas, Scipy, and scikit-learn, and document problem statements clearly with verified correct answers.

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Contributors design original computational STEM problems that simulate real scientific workflows, create problems that require Python programming to solve, and ensure these problems are computationally intensive and cannot be solved manually within reasonable timeframes. They develop problems requiring non-trivial reasoning chains and creative problem-solving approaches, verify solutions using Python with standard libraries such as Numpy, Pandas, Scipy, and scikit-learn, and document problem statements clearly while providing verified correct answers.

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Contributors may design original computational STEM problems that simulate real scientific workflows, create problems requiring Python programming to solve, ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes, develop problems requiring non-trivial reasoning chains and creative problem-solving approaches, verify solutions using Python with standard libraries such as Numpy, Pandas, Scipy, and scikit-learn, and document problem statements clearly while providing verified correct answers.