ML Engineer

Member of Technical Staff, North Modelling (Evals) Build evaluation systems, feedback loops, and applied modelling workflows to ensure model progress translates into better product outcomes for North users. Define and own the eval strategy for North, create high-quality evals from product realities, and continuously update evals based on user and customer feedback. Extract insights from eval results and product context to guide model selection, patches, and updates. Collaborate closely with product, customer-facing, and modelling teams to define success metrics and actionable recommendations.

Design and build end-to-end agentic systems for creative tasks; Develop novel approaches for training and adapting large language models for multimodal creative tasks; Design new objectives, datasets, and fine-tuning strategies to improve agent behavior and reliability; Explore multimodal reasoning and structured generation for creative control; Run systematic experiments to evaluate and improve agent performance in real-world tasks; Design evaluation frameworks for agentic workflows in video analysis and editing; Analyze failure modes across the full agent loop and iterate on improvements.

Train and optimize large-scale video and multimodal models; Improve efficiency across training and inference (memory, latency, cost); Implement techniques such as distillation, quantization, and pruning to accelerate diffusion and autoregressive generation; Build and maintain distributed training systems; Optimize GPU utilization, parallelism, and throughput; Develop tooling for experimentation, evaluation, and debugging; Translate research models into robust, production-ready systems; Monitor and improve model performance in real-world usage.

• Own ML projects end to end within a product team, including framing the problem, choosing the approach, executing the plan, shipping the feature, and monitoring its performance in production.
• Work closely with SMB, Scaler, or Enterprise product teams on focused ML problems tied to clear customer and business outcomes.
• Make build vs. buy decisions for ML models, deciding when to fine-tune or build models versus integrating external models or APIs.
• Move quickly from idea to production, prioritizing rapid iteration and customer impact.
• Collaborate with the ML team to leverage shared infrastructure, evaluation tooling, and expertise.
• Stay close to users by getting rapid feedback, monitoring usage, and iterating based on insights.
• Balance technical depth with pragmatism, knowing when deeper modeling is necessary and when existing solutions suffice.
• Write production-quality code directly in the product team's codebase.
• Own quality in production by building sound evaluations, monitoring performance, and investigating failures post-launch.

Set and evolve the research direction for A1's core intelligence, including context representation, memory, reasoning, planning, and orchestration. Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models. Define evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term behavior. Own alignment, safety, and guardrail strategy as first-class product concerns. Guide exploration of frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal systems. Shape early product intelligence direction in close partnership with product and application engineering. Set the technical bar for research rigor, judgment, and taste across the organization.

Set and evolve the research direction for A1's core intelligence, including context representation, memory, reasoning, planning, and orchestration. Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models. Define evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term behavior. Own alignment, safety, and guardrail strategy as first-class product concerns. Guide exploration of frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal systems. Shape early product intelligence direction in close partnership with product and application engineering. Set the technical bar for research rigor, judgment, and taste across the organization.

• Own ML projects end to end within a product team, including framing the problem, choosing the approach, executing the plan, shipping the feature, and monitoring its performance in production.
• Work closely with SMB, Scaler, or Enterprise product teams on focused ML problems tied to clear customer and business outcomes.
• Make build vs. buy decisions for ML models, deciding when to fine-tune or build models versus integrating external models or APIs.
• Move quickly from idea to production, prioritizing rapid iteration and customer impact.
• Collaborate with the ML team to leverage shared infrastructure, evaluation tooling, and expertise.
• Stay close to users by getting rapid feedback, monitoring usage, and iterating based on insights.
• Balance technical depth with pragmatism, knowing when deeper modeling is necessary and when existing solutions suffice.
• Write production-quality code directly in the product team's codebase.
• Own quality in production by building sound evaluations, monitoring performance, and investigating failures post-launch.

• Own ML projects end to end within a product team, including framing the problem, choosing the approach, executing the plan, shipping the feature, and monitoring its performance in production.
• Work closely with SMB, Scaler, or Enterprise product teams on focused ML problems tied to clear customer and business outcomes.
• Make build vs. buy decisions for ML models, deciding when to fine-tune or build models versus integrating external models or APIs.
• Move quickly from idea to production, prioritizing rapid iteration and customer impact.
• Collaborate with the ML team to leverage shared infrastructure, evaluation tooling, and expertise.
• Stay close to users by getting rapid feedback, monitoring usage, and iterating based on insights.
• Balance technical depth with pragmatism, knowing when deeper modeling is necessary and when existing solutions suffice.
• Write production-quality code directly in the product team's codebase.
• Own quality in production by building sound evaluations, monitoring performance, and investigating failures post-launch.

Design and evolve critical ML systems across training, inference, evaluation, and infrastructure. Architect and build large-scale ML systems including training pipelines, inference systems, and data systems. Implement evaluation pipelines for performance, robustness, safety, and bias. Own production deployment with GPU optimization, memory efficiency, latency reduction, and scaling. Collaborate with engineering teams to integrate ML systems into products. Make pragmatic trade-offs and ship improvements quickly under production constraints.

• Own ML projects end to end within a product team, including framing the problem, choosing the approach, executing the plan, shipping the feature, and monitoring its performance in production.
• Work closely with SMB, Scaler, or Enterprise product teams on focused ML problems tied to clear customer and business outcomes.
• Make build vs. buy decisions for ML models, deciding when to fine-tune or build models versus integrating external models or APIs.
• Move quickly from idea to production, prioritizing rapid iteration and customer impact.
• Collaborate with the ML team to leverage shared infrastructure, evaluation tooling, and expertise.
• Stay close to users by getting rapid feedback, monitoring usage, and iterating based on insights.
• Balance technical depth with pragmatism, knowing when deeper modeling is necessary and when existing solutions suffice.
• Write production-quality code directly in the product team's codebase.
• Own quality in production by building sound evaluations, monitoring performance, and investigating failures post-launch.

Own end-to-end ML system execution including data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods. Architect and operate scalable inference systems balancing latency, cost, and reliability. Design and maintain data systems for high-quality training data. Implement evaluation pipelines for performance, robustness, safety, and bias. Own production deployment with GPU optimization, memory efficiency, latency reduction, and scaling policies. Collaborate with engineering teams to integrate ML systems into products. Make pragmatic trade-offs and ship improvements quickly under real production constraints.

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What does a Machine Learning Engineer do?

Machine Learning Engineers design, build, and deploy AI systems that solve real-world problems. They transform research prototypes into production-ready solutions by creating scalable ML pipelines, optimizing model performance, and handling data preprocessing workflows. They integrate models with applications via APIs, implement monitoring systems, and ensure models perform reliably in production environments. Daily tasks include collaborating with data scientists, fine-tuning algorithms, building deployment infrastructure . click apply for full job details