Automotive Engineering & Python Expert - Freelance AI Trainer
Contributors design graduate- and industry-level automotive engineering problems grounded in real practice; evaluate AI-generated solutions for correctness, assumptions, and engineering logic; validate analytical or numerical results using Python (NumPy, SciPy, Pandas); improve AI reasoning to align with first principles and accepted engineering standards; and apply structured scoring criteria to assess multi-step problem solving.
Senior Software Engineer
You will be building a powerful project innovating the world of customer support by defining what an AI-first SaaS product looks like, including its UI/UX, capabilities, and data models. You will take ownership of challenging problems and define and implement solutions. The role involves working across the tech stack, leading ambitious and ambiguous projects that require strong technical decision-making, effective implementation, and good product and design instincts. Additionally, you will mentor and lead less experienced engineers.
Product Manager, Models
Own product strategy and roadmap for Heidi's models platform including evaluation, safety, model routing, and fine-tuning infrastructure, setting clear goals and being accountable to achieving them. Prioritise the team's work across enablement requests, model safety and quality, and new capability bets. Identify and resolve where product teams get stuck on models by fixing the platform. Build evaluation tooling and fine-tuning workflows usable by engineers and product teams in clinical settings. Decide improvements based on clinician feedback, model quality signals, and product team requests. Allocate engineering capacity among competing product teams and communicate deferrals clearly. Collaborate with engineers on evaluation design, fine-tuning decisions, and model architecture at a technical level. Set model quality and safety targets grounded in clinical outcomes. Consolidate infrastructure duplicated across product teams. Monitor foundation model developments and update the roadmap accordingly. Reporting into Product leadership, this platform role supports every user-facing product at Heidi.
Senior ML Operations (MLOps) Engineer
The Senior ML Operations (MLOps) Engineer at Eight Sleep is responsible for introducing and implementing cutting-edge ML technologies, owning the design and operation of robust ML infrastructure including scalable data, model, and deployment pipelines to ensure reliable model delivery to production. They collaborate cross-functionally with R&D, firmware, data, and backend teams to ensure reliable and scalable ML inference on Pods. They optimize ML systems for cost, scalability, and performance across training and inference, and develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment. The role requires effective communication in a remote work environment.
Manual Quality Assurance Engineer, Web Core Product
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to 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 that improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Senior Software Engineer - Australia
As a Senior Software Engineer at Neara, you will design and implement features in one of three engineering groups: Digitisation, Platform, or Design. In Digitisation, responsibilities include developing and measuring algorithmic and machine learning improvements to digital twin extraction, improving monitoring systems to identify extraction errors, and creating tooling to quickly fix problems from automated solutions. In Platform, duties involve developing the internal platform functionality by identifying common abstractions for varied use cases and creating data abstractions that allow users to semantically model and interact with organizational data. In Design, tasks include developing solutions for simulating structural forces on electric networks and their behavior in different weather scenarios, as well as developing CAD-like tools to import engineering designs and integrating lidar and imagery for better simulation outcomes.
Senior Software Engineer (Backend - Scribe)
As a Senior Software Engineer working closely with the Engineering Manager for Transcription and Note Generation, you will develop AI-powered solutions that transform clinical conversations into structured medical documentation. Your responsibilities include designing and developing robust, scalable systems for real-time processing and examination of clinical conversations, collaborating with the AI/ML team to integrate and optimize medical speech transcription and note generation, designing, building, and maintaining APIs and services for transcription and note generation capabilities, implementing systems to ensure high accuracy and reliability in medical documentation generation, optimizing performance for real-time results in clinical settings, and contributing to the overall architecture and best practices for scalable and maintainable systems.
Safety Engineer
The AI Safety Engineer is responsible for designing and building scalable backend infrastructure for content moderation, abuse detection, and agents guardrails by deploying AI/ML models into production systems. They will architect robust APIs, data pipelines, and service architectures to support real-time and batch moderation workflows. The role includes implementing comprehensive monitoring, alerting, and observability systems, establishing SLIs, SLOs, and performance benchmarks. The engineer will collaborate with ML engineers to translate research models into production-ready systems and integrate them across the product suite. Additionally, they will drive technical decisions and contribute to the vision for the safety roadmap to build next-generation platform guardrails for scale and precision.
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.
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.
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