This opportunity is available through a leading AI-driven work platform.
Machine Learning Systems Evaluation Engineer
Location
United States
Posted
4 days ago
Salary
$90 / hour
Seniority
Mid Level
No structured requirement data.
Job Description
Machine Learning Systems Evaluation Engineer
24-MAG
Role Description We are sharing a specialised remote consulting opportunity for experienced machine learning engineers with strong coding agent experience, production ML judgment, and the ability to evaluate complex machine learning and AI engineering implementations across realistic technical scenarios. This role supports current and upcoming remote consulting opportunities focused on machine learning system evaluation, coding-agent-assisted technical workflows, ML implementation review, inference system assessment, MLOps evaluation, and LLM application analysis. Selected professionals may use tools such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or comparable coding agents to complete, review, and evaluate technical tasks involving model training, deployment infrastructure, inference workflows, AI-powered products, and production machine learning systems. Key Responsibilities - Machine Learning Implementation Review - Use modern coding agents to complete and evaluate complex machine learning and AI engineering tasks - Review generated implementations involving model training, inference systems, MLOps workflows, LLM applications, and AI-powered product features - Assess technical outputs for correctness, quality, maintainability, performance, reliability, and production-readiness - Apply professional machine learning engineering judgment to realistic technical scenarios - MLOps, Deployment & Inference Evaluation - Evaluate ML system workflows involving model deployment, inference infrastructure, monitoring, testing, and production integration - Review implementation choices related to scalability, latency, data flow, model serving, reliability, and system maintainability - Identify bugs, edge cases, performance issues, failure modes, and weak assumptions in ML engineering outputs - Provide structured feedback on MLOps design, deployment patterns, and production ML system quality - Coding Agent Output Assessment - Compare outputs from multiple coding agents and assess their strengths, weaknesses, accuracy, and practical usefulness - Identify where generated solutions succeed, where they fail, and where additional ML engineering judgment is required - Evaluate whether generated machine learning implementations reflect real-world engineering standards - Document technical review findings clearly for project teams and quality evaluation workflows - Technical Documentation & Feedback - Produce clear, structured evaluations of machine learning engineering tasks and generated outputs - Explain reasoning around model training, inference systems, deployment infrastructure, LLM applications, performance, and architectural trade-offs - Support technical assessment workflows by documenting accepted work, improvement areas, and practical engineering conclusions - Help ensure outputs reflect production-scale machine learning engineering expectations Qualifications - 2+ years of professional machine learning engineering experience - Hands-on experience building production ML systems, model deployment infrastructure, LLM applications, or AI-powered products - Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or comparable tools - Ability to evaluate generated machine learning implementations and identify technical trade-offs, bugs, edge cases, and performance issues - Experience deploying ML systems to production is strongly preferred - Strong understanding of model training, inference workflows, MLOps, data pipelines, evaluation methods, deployment patterns, and system reliability - Clear written communication skills and comfort documenting technical reasoning in a remote, project-based environment Educational Background - A degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Software Engineering, Computer Engineering, Statistics, Mathematics, or a related technical field is helpful - Equivalent professional experience in machine learning engineering, applied AI, MLOps, LLM applications, or production ML systems is also highly relevant Nice to Have - Experience with Python, PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, LlamaIndex, MLflow, Ray, or comparable ML tools - Familiarity with model serving, feature pipelines, vector databases, embeddings, retrieval systems, LLM application architecture, or evaluation frameworks - Experience with cloud platforms, Docker, Kubernetes, CI/CD pipelines, observability tooling, or production deployment workflows - Background in technical code review, ML architecture review, model performance evaluation, or large-scale AI product engineering - Strong comfort working in sprint-based project environments with focused technical assessment windows Why This Opportunity - Remote consulting work aligned with machine learning engineering, coding agent, and technical evaluation expertise - Opportunity to evaluate realistic ML engineering workflows involving model training, inference systems, MLOps, LLM applications, and production AI systems - Suitable for engineers who enjoy technical assessment, tool-assisted coding workflows, ML implementation review, and practical system-level problem-solving - Sprint-based project work that can align with focused availability and remote schedules Contract Details - Independent contractor engagement - Fully remote and flexible scheduling - Sprint-based, project-based availability - Some project work may run in focused 12–24 hour sprint windows depending on project requirements - Compensation may reach up to $90/hour, depending on project scope, experience, and accepted work structure - Some projects may use accepted-task compensation depending on the specific workflow - Payments are made weekly via Stripe or Wise based on services rendered - Projects may be extended, shortened, adjusted, or concluded based on project needs and performance - Candidates requiring H1-B or STEM OPT sponsorship support are not eligible at this time - Work must not involve sharing confidential or proprietary information from any employer, client, or institution
Related Guides
Related Job Pages
More Machine Learning Engineer Jobs
• Operationalize machine learning models by building and maintaining robust, scalable pipelines for training, evaluation, deployment, and lifecycle management across cloud, on-prem, and edge compute environments • Work closely with autonomy researchers, software engineers, systems teams, and field operators to translate mission requirements into deployable ML capabilities • Implement automated CI/CD workflows tailored to ML systems, ensuring repeatable experiments, reliable packaging, and continuous delivery of both up to date models and associated data pipelines • Manage ML runtime infrastructure using containerization and orchestration frameworks (e.g., Docker, Kubernetes) and incorporating model serving platforms (e.g., Seldon, KServe, BentoML) • Develop monitoring systems to track model health, performance, data drift, system utilization, and mission relevance using tools such as Prometheus, Grafana, and ELK/EFK stacks • Ensure ML deployments meet defense, customer, and platform security requirements, with emphasis on data integrity, traceability, and operational reliability • Evaluate and integrate emerging MLOps, distributed training, and edge inference technologies to enhance reproducibility, extensibility, scalability, and deployment speed of ML systems
Lead Machine Learning Engineer – Team Lead
DatatonicGoogle Cloud AI+ML Partner of the Year. We drive business impact through innovative cloud engineering, analytics and AI.
• Provide strategic and technical leadership to the ML team • Recruit, hire, and onboard top talent, including data scientists and machine learning engineers • Offer mentorship and guidance, helping team members develop their technical skills • Oversee and manage complex ML projects from initial concept to successful deployment • Build and maintain strong, trusted relationships with clients • Actively participate in the sales process as a subject matter expert
Role Description Join our innovative team and help us in shaping the future of Technology! - Design, develop, and deploy AI/ML solutions to modernize core pharmacy platforms, with a focus on scalability, reliability, performance, and security. - Leverage Generative AI, LLMs, and agentic AI frameworks to automate and enhance pharmacy workflows and decision-making processes. - Collaborate with business and technical stakeholders to understand pharmacy domain requirements and translate them into robust AI/ML-driven technical solutions. - Contribute to architecture and technical design of AI/ML pipelines, including model selection, data integration, and deployment patterns. - Establish and maintain AI/ML engineering standards, best practices, and quality assurance processes. - Conduct code reviews, provide constructive feedback, and mentor engineers on modern AI/ML engineering practices. - Partner with cross-functional teams including data scientists, product managers, platform engineers, and pharmacy domain experts. - Translate legacy system modernization needs into scalable AI applications that enhance products, workflows, and operational efficiency. - Monitor and optimize AI/ML model performance, resource utilization, and platform reliability. - Collaborate with DevOps and infrastructure teams to ensure secure, scalable deployment and operations of AI/ML solutions. - Stay current with advancements in AI/ML frameworks, Generative AI, LLMs, and pharmacy technology modernization. Qualifications - 5+ years of experience as a software engineer or AI/ML engineer delivering cloud-based solutions. - 5+ years of experience with cloud platforms such as Azure, AWS, or Google Cloud. - 5+ years of programming experience in Python, Golang, JavaScript, or Java. - 3+ years of hands-on experience with AI/ML frameworks, model development, and deployment. - 3+ years of hands-on experience with Generative AI, LLMs, and agentic AI solutions. - Experience building distributed systems and cloud-native AI/ML applications. - 1+ years of experience with security and compliance in cloud environments. - Strong problem-solving and analytical skills with the ability to propose innovative AI/ML solutions. - Excellent communication and collaboration skills across technical and non-technical teams. Company Description VXForward is a leading technology solutions provider that helps to create a business value with innovative and cost-effective services across different industries. - Exciting Projects, Meaningful Impact - Continuous Learning and Growth - State-of-the-Art Technologies - Collaborative Team Environment - Make a Real Difference - Work-Life Balance
ML Microarchitect
WaymoWaymo is an autonomous driving technology company creating a new way forward in mobility.
Title: ML Microarchitect Location: Mountain View, California, United States Full-Time Hardware Engineering ID: 4594 Job Description: Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver-The World's Most Experienced Driver-to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo's fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Waymo's Compute Team is tasked with a critical and exciting mission: We deliver the compute platform responsible for running the fully autonomous vehicle's software stack. To achieve our mission, we architect and create high-performance custom silicon; we develop system-level compute architectures that push the boundaries of performance, power, and latency; and we collaborate closely with many other teammates to ensure we design and optimize hardware and software for maximum performance. We are a multidisciplinary team seeking curious and talented teammates to work on one of the world's highest performance automotive compute platforms. This role follows a hybrid work schedule and you will report to the Senior Staff Silicon Engineer. You will: - Work with researchers and architects to translate high level requirements into hardware features - Specify and design microarchitectures to deliver world class ML performance - Perform power, area and performance exploration and optimization of digital designs - Design high performance execution units, arithmetic circuits and programmable engines - Work with verification teams to guarantee functional correctness and performance You have: - BS degree in Computer Engineering or equivalent practical experience - 5+ years of industry experience with SystemVerilog, RTL design and microarchitecture - 3+ years designing and specifying microarchitectures of high performance computing cores (CPU/GPU/NPU) - Fluency in at least one high level programming language such as Python, C++ We prefer: - Experience designing datapath elements of high performance cores (CPU/GPU/NPU) - Experience working with Chisel (Scala) or other higher-level hardware DSLs - Working knowledge of machine learning algorithms & how they map to hardware - Familiarity with Synthesis and power analysis tools - Experience working with formal tools for datapath verification The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo's discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $175,000-$215,000 USD We appreciate your interest in Waymo. Waymo is proud to be an equal opportunity employer, committed to creating a culture of belonging and maintaining a supportive workplace for all employees. We welcome applicants of all backgrounds, and employment decisions are based on a candidate's qualifications, experience, and alignment with job requirements and business needs. Waymo does not discriminate against, and prohibits harassment of, any applicant or employee based on race, color, sex, sexual orientation, gender identity, religion, national origin, age, disability, military status, family status, pregnancy, genetic information or any other basis protected by applicable law. Waymo will also consider for employment qualified applicants with criminal records in accordance with applicable law. Waymo is committed to making sure our hiring process is accessible for all candidates.



