Machine Learning Platform Engineer

As an ML Platform Engineer, you will build the infrastructure and systems that power A1’s AI capabilities.

You will design and operate the systems behind the AI stack, from model training and evaluation to deployment, inference, observability, and continuous improvement.

You will work closely with AI engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems. You will build the platforms, tooling, and infrastructure that enable the team to experiment quickly and bring AI capabilities to production with confidence.

Focus

• Build and operate the ML infrastructure and platforms powering A1’s AI products
• Design systems for model training, evaluation, deployment, inference, and experimentation
• Build and optimise model serving and inference infrastructure for high-throughput and low-latency workloads
• Improve reliability, scalability, latency, and cost efficiency of AI systems
• Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement
• Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster
• Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions
• Build production observability, monitoring, tracing, and alerting for AI/ML workloads
• Improve AI systems across reliability, scalability, latency, throughput, and cost
• Identify bottlenecks across the ML stack and continuously improve system performance
• Work closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure

Tech Stack

• Python
• PyTorch / JAX
• LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM
• Cloud infrastructure
• Distributed systems
• ML/data pipelines and workflow orchestration
• GPU infrastructure and performance tooling
• Vector databases and retrieval infrastructure, Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders. Top Skills: Computer Vision/BiometricsGenerative AiGraph Neural Networks (Gnns)LlmsModel Serving InfrastructureNlpPythonPyTorchSequence ModelsTensorFlow General Motors

Machine Learning Engineer

18 Days Ago Remote or Hybrid 171K-261K Annually Senior level 171K-261K Annually Senior level Automotive * Big Data * Information Technology * Robotics * Software * Transportation * Manufacturing Lead development and operation of CI platform components (RBE, FUSE) to enable scalable build, test, and developer workflows. Collaborate across teams, own projects end-to-end, drive engineering best practices, mentor engineers, perform design/code reviews, and support integration of internal tools with the FUSE-based file system to improve developer productivity for AV and ML workloads. Top Skills: FuseGoLinuxNetworkingPythonRemote Build Execution (Rbe)Ssh Brain Co.

Machine Learning Engineer

7 Days Ago Remote or Hybrid Mid level Mid level Artificial Intelligence * Information Technology * Software Build core, shared ML capabilities for a production platform: foundation models for documents, extraction agents, model routing, unified evaluation, and continuous improvement pipelines. Own end-to-end capability development, production deployment, and cross-team support to improve accuracy, latency, cost, and reliability across institutional workflows. Top Skills: Agentic SystemsDocument ExtractionEvaluation SystemsFoundation ModelsLlmsModel RoutingRl Fine-TuningSegmentation ModelsVision ModelsVlms

What you need to know about the Colorado Tech Scene

With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.

Key Facts About Colorado Tech

• Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
• Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
• Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
• Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
• Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
• Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute

• Strong software engineering fundamentals and experience building production systems
• Experience building ML infrastructure, platforms, or production machine learning systems
• Experience with model deployment, inference, evaluation, or data pipelines
• Strong understanding of distributed systems and system reliability
• Ability to write clean, maintainable, production-quality code
• Comfortable working in ambiguous, fast-moving environments
• Bias toward ownership, experimentation, and continuous improvement

Outcomes

• AI infrastructure reliably supports production workloads at scale
• Models can be trained, evaluated, deployed, and improved efficiently
• Inference systems deliver strong latency, throughput, reliability, and cost efficiency
• ML pipelines are reproducible, observable, maintainable, and robust
• Model and infrastructure regressions are detected quickly and diagnosed efficiently
• Common ML infrastructure capabilities become reusable platform primitives rather than being rebuilt for every AI product
• The AI stack can evolve rapidly as new models, architectures, and inference techniques emerge

Posted 8 Hours Ago Remote Hiring Remotely in United States Mid level Remote Hiring Remotely in United States Mid level Design, build, and operate ML infrastructure for training, evaluation, deployment, and inference. Improve reliability, scalability, latency, throughput, and cost. Create pipelines, observability, benchmarking, and tooling to enable fast experimentation and productionization of models while diagnosing regressions and bottlenecks. The summary above was generated by AI

About A1

There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.

Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.