Senior Machine Learning Scientist
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.
Machine Learning Engineer (Semantic Scene Understanding)
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.
Director, Data Center Operations
The responsibilities include advancing inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implementing and maintaining changes in high-performance inference engines, including kernel backends and speculative decoding, profiling and optimizing performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Unifying inference with RL/post-training by designing and operating RL and post-training pipelines, making RL and post-training workloads more efficient with inference-aware training loops, and using these pipelines to train, evaluate, and iterate on frontier models. Co-designing algorithms and infrastructure so that objectives, rollout collection, and evaluation are tightly coupled to efficient inference, identifying bottlenecks across the training engine, inference engine, data pipeline, and user-facing layers. Running ablations and scale-up experiments to understand trade-offs between model quality, latency, throughput, and cost, and feeding these insights back into model, RL, and system design. Owning critical systems at production scale by profiling, debugging, and optimizing inference and post-training services under real production workloads, driving roadmap items requiring engine modification, and establishing metrics, benchmarks, and experimentation frameworks to validate improvements rigorously. Providing technical leadership by setting technical direction for cross-team efforts at the intersection of inference, RL, and post-training, and mentoring other engineers and researchers on full-stack ML systems work and performance engineering.
Staff Analytics Engineer — Data Warehouse
Advance inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implement and maintain changes in high-performance inference engines, including kernel backends, speculative decoding, and quantization. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Design and operate RL and post-training pipelines where most cost is inference, jointly optimizing algorithms and systems. Make RL and post-training workloads more efficient with inference-aware training loops, async RL rollouts, and speculative decoding. Use these pipelines to train, evaluate, and iterate on frontier models. Co-design algorithms and infrastructure to tightly couple objectives, rollout collection, and evaluation with efficient inference, identifying bottlenecks across the training engine, inference engine, data pipeline, and user-facing layers. Run ablations and scale-up experiments to understand trade-offs between model quality, latency, throughput, and cost, feeding insights into model, RL, and system design. Profile, debug, and optimize inference and post-training services under real production workloads. Drive roadmap items requiring engine modification such as changing kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously. Provide technical leadership to set direction for cross-team efforts in inference, RL, and post-training and mentor engineers and researchers on full-stack ML systems work and performance engineering.
Sr. Partnerships Manager, Model Ecosystem
Advance inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference; implementing and maintaining changes in high-performance inference engines such as SGLang- or vLLM-style systems and Together’s inference stack, including kernel backends, speculative decoding, and quantization; profiling and optimizing performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Unify inference with RL/post-training by designing and operating RL and post-training pipelines such as RLHF, RLAIF, GRPO, and DPO-style methods where most cost is inference, jointly optimizing algorithms and systems; making workloads more efficient with inference-aware training loops, async RL rollouts, and speculative decoding; training, evaluating, and iterating on frontier models; co-designing algorithms and infrastructure to tightly couple objectives, rollout collection, and evaluation to efficient inference; running ablations and scale-up experiments to understand trade-offs and feed insights into model, RL, and system design. Own critical systems at production scale by profiling, debugging, and optimizing inference and post-training services; driving roadmap items involving engine modifications like changing kernels, memory layouts, scheduling logic, and APIs; establishing metrics, benchmarks, and experimentation frameworks to validate improvements rigorously. Provide technical leadership by setting technical direction for cross-team efforts at the intersection of inference, RL, and post-training; mentoring engineers and researchers on full-stack ML systems work and performance engineering.
Machine Learning Engineer, Anonymization
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.
US Sales and Partnerships Lead, Digital Diagnostics
Lead the team responsible for the AI/ML Stack infrastructure that bridges ML research and production, evolving the stack to meet large scale ML training and inference workload needs. Develop and execute a long-term vision and roadmap for the MLOps team to support ML development and deployment needs across business units, managing short-term deliveries and long-term architectural transformation. Lead and mentor a team of 6-7+ engineers, strategically allocate resources for support and strategic initiatives. Collaborate cross-functionally with leaders in machine learning, data science, product engineering, and infrastructure to identify pain points, address bottlenecks, and facilitate deployment of new solutions. Architect compute and storage pipelines to manage millions of slides and complex artifacts without data fragmentation or latency. Modernize AI product inference stack to support substantial growth in AI runs globally. Work with Site Reliability Engineering to establish comprehensive system observability metrics including compute utilization, network bottlenecks, and cost attribution. Conduct build versus buy assessments and lead stack refresh audits to benchmark proprietary tools against commercial and open-source alternatives.
Machine Learning Intern (202641)
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.
Applied ML Engineer, Data
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.
Machine Learning Engineer
Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference. Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies. Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques. Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation. Apply machine learning design patterns to build modular, reusable, and production-ready models. Collaborate with data engineers to develop high-performance data pipelines for training and inference. Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes. Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques.
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