Freelance Electrical Engineer with Python Experience - AI Trainer
Contributors may design rigorous electrical engineering problems reflecting professional practice, evaluate AI solutions for correctness, assumptions, and constraints, validate calculations or simulations using Python (NumPy, Pandas, SciPy), improve AI reasoning to align with industry-standard logic, and apply structured scoring criteria to multi-step problems.
AI Engineer
As an AI Engineer, you will lead the development of high-impact AI systems, architect how the AI detects risks in real-time, and ensure the search infrastructure is robust, fast, and accurate. You take full accountability for the AI systems you build, from initial discovery and RAG architecture to production deployment and monitoring. You challenge the purpose and methods to ensure the AI effectively solves user problems with high precision. You will architect hybrid search methods combining vector databases and LLMs, and build scalable backend logic. You raise the team’s standards through benchmarking, continuous model evaluation, and maintaining a "ship fast, but never broken" mentality. You also provide mentorship, fostering a culture of curiosity, precision, and continuous learning in the AI space.
Senior Machine Learning Engineer - Australia
As a Senior Machine Learning Engineer at Neara, you will create machine learning models that drive the digitisation of real-world infrastructure from various data sources such as LIDAR, imagery, and vector data. You will work at every stage of the ML lifecycle, including data collection, quality assurance, training, and model monitoring. You will decide which problems are suitable for machine learning solutions, define the ML strategy, and stay updated with best practices in data handling, MLOps, and the latest advancements in machine learning to integrate new techniques into the platform. Responsibilities also include developing approaches to generate accurate electric networks from imperfect data using deep learning and classical ML algorithms, developing and optimizing training pipelines, scaling model serving for different problems, improving model QA speed and identifying data and distribution drift, working with diverse data sources and building scalable data pipelines for training and serving, and mentoring junior engineers in best practices for model training and software engineering.
Sales Development Representative, APAC
As an AI Quality Assurance Intern, you will execute structured test plans for AI Agent implementations, validate that intents, entities, and workflows trigger correctly, identify behavioral inconsistencies, misclassifications, or hallucinations, review and validate AI-generated suggestions, summaries, and classifications, provide actionable feedback to improve intent models and redaction/PII handling, run bias, redaction, and transcription accuracy checks, spot patterns or emerging issues across test sets, and contribute ideas for improving internal QA processes and tools. You will help ensure our AI systems act as intended, follow correct business logic, and provide accurate and safe outputs to end users.
Signal Engineer
The Signal Engineer is responsible for designing, sourcing, shaping, and evaluating input signal at scale to enable AI models to learn faster, learn better, and generalize more effectively. This includes working deeply with raw and synthetic data, designing new datasets and signal mixtures, iterating on input distributions and formats, understanding how small changes in signal affect training behavior, thinking across stages of training rather than in isolation, and treating data as a dynamic, evolving system rather than a static asset. The role involves immersion, experimentation, and developing intuition through long feedback loops, using computational environments and technical tooling, working directly with data through scripts, notebooks, and experimental pipelines, modifying existing systems, and building small utilities to explore questions.
AI / ML Solutions Engineer
The AI / ML Solutions Engineer at Anyscale is responsible for designing, implementing, and scaling machine learning and AI workloads using Ray and Anyscale directly with customers. This includes implementing production AI / ML workloads such as distributed model training, scalable inference and serving, and data preprocessing and feature pipelines. The role involves working hands-on with customer codebases to refactor or adapt existing workloads to Ray. The engineer advises customers on ML system architecture including application design for distributed execution, resource management and scaling strategies, and reliability, fault tolerance, and performance tuning. They guide customers through architectural and operational changes needed to adopt Ray and Anyscale effectively. Additionally, the engineer partners with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows, supports CI/CD, monitoring, retraining, and operational best practices, and helps customers transition from experimentation to production-grade ML systems. They also enable customer teams through working sessions, design reviews, training delivery, and hands-on guidance, contribute feedback to product, engineering, and education teams, and help develop reference architectures, examples, and best practices based on real customer use cases.
Software Engineer, macOS Core Product - Virginia Beach, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability, then design and implement solutions addressing the highest priority issues.
Software Engineer, macOS Core Product - Rialto, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to their customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to gain visibility into bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Software Engineer, macOS Core Product - Waco, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability, and design and implement solutions to address the highest priority issues.
Software Engineer, macOS Core Product - Sydney, Australia
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Access all 4,256 remote & onsite AI jobs.
Frequently Asked Questions
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.