At Scale, our mission is to accelerate the development of AI applications. We're looking for a Machine Learning Research Scientist/Engineer to join our team and help us shape the future of AI.

This role operates at the forefront of AI research and real-world implementation, with a strong focus on reasoning within large language models (LLMs). You will study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents. You will play a key role in shaping Scale's data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning.

Success in this role requires a deep understanding of LLMs, planning algorithms, and novel approaches to agentic reasoning, as well as creativity in tackling challenges related to data generation, model interaction, and evaluation. You will contribute to impactful research on language model reasoning, collaborate with external researchers, and work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions.

Responsibilities

• Study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents
• Shape Scale's data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning
• Contribute to impactful research on language model reasoning
• Collaborate with external researchers
• Work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions

Requirements

• Practical experience working with LLMs, with proficiency in frameworks like PyTorch, JAX, or TensorFlow
• A track record of published research in top ML and NLP venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, CoLLM, etc.)
• At least three years of experience solving complex ML challenges, either in a research setting or product development, particularly in areas related to LLM capabilities and reasoning
• Strong written and verbal communication skills, along with the ability to work effectively across teams

Nice to Have

• Hands-on experience fine-tuning open-source LLMs or leading bespoke LLM fine-tuning projects using PyTorch/JAX
• Research and practical experience in building applications and evaluations related to LLM-based agents, including tool-use, text-to-SQL, browser agents, coding agents, and GUI agents
• Experience with agent frameworks such as OpenHands, Swarm, LangGraph, or similar
• Familiarity with advanced agentic reasoning techniques such as STaR and PLANSEARCH
• Proficiency in cloud-based ML development, with experience in AWS or GCP environments

Benefits

• Comprehensive health, dental and vision coverage
• Retirement benefits
• A learning and development stipend
• Generous PTO
• Commuter stipend

Salary Range

$252,000-$315,000 USD

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As a senior member of the team, you will champion modern, AI-enhanced quality practices and help build a culture where continuous improvement, automation-first thinking, and data-driven decisions are embedded at every stage of product development. This is a hybrid position in Mountain View (Headquarters) and will require in-office work 2 days a week.

The base salary range for this full-time position is $210,000 to $257,000, plus equity and benefits. Our salary ranges are determined by role, level, and location. EarnIn provides excellent benefits for our employees, including healthcare, internet/cell phone reimbursement, and a learning and development stipend.

Quality Engineering & Test

Own end-to-end quality across iOS and Android applications and their supporting backend services, ensuring high confidence in weekly (or faster) releases. Design and implement comprehensive test strategies covering:

• Native mobile applications (iOS & Android)
• Mobile-to-backend integrations (REST APIs, auth flows, event-driven systems)
• Microservices and distributed systems
• Critical web workflows that intersect with mobile journeys
• Device, OS, browser, and network variability
• App lifecycle events, offline behavior, retries, and edge cases

Ensure critical user journeys are validated across mobile UI → API → backend → web touchpoints, preventing production escapes in high-impact flows. Partner with engineering teams to embed quality gates into the mobile release lifecycle, including pre-merge validation, release candidate verification, and post-deploy smoke testing.

Drive improvements in testability by introducing better logging, API contracts, observability hooks, feature flags, and deterministic state management. Establish meaningful quality metrics (crash analytics, defect trends, flaky tests, API reliability, release risk scoring) and surface actionable insights to engineering stakeholders.

Champion shift-left quality by influencing design reviews, API schema discussions, and acceptance criteria early in development.

AI-Driven Quality and Automation

Leverage AI to enhance mobile, backend, and web testing effectiveness, including:

• AI-assisted test case and test data generation
• Intelligent regression suite prioritization based on code changes
• Predictive defect detection and risk-based testing
• Flaky test detection and automated stabilization insights

Integrate AI-powered log intelligence, crash clustering, and anomaly detection into quality workflows. Continuously evaluate and experiment with AI-driven QA tools to increase coverage, reduce maintenance overhead, and accelerate release cycles.

Contribute to building an AI-augmented quality ecosystem that improves speed without compromising reliability.

Automation Excellence

Design, build, and scale robust automation frameworks using:

• XCUITest, Espresso, Appium (mobile automation)
• Playwright (web and mobile web validation)
• REST Assured or similar tools for API and service validation

Ensure frameworks are modular, maintainable, and optimized for scale across multiple teams. Integrate automated validation into CI/CD pipelines (Jenkins, GitHub Actions, etc.) to enable:

• Pre-merge quality gates
• Parallelized execution
• Environment-aware test runs
• Post-deployment smoke and regression coverage

Build developer-friendly tooling that enables:

• Self-service test execution
• Real-time reporting and dashboards
• Faster debugging and failure triage
• Scalable test data and environment management

Continuously reduce flakiness, improve signal quality, and optimize execution time across mobile and backend suites.

Performance, Scalability & Reliability

Design and execute performance validation across:

• Mobile app startup time and responsiveness
• API latency, throughput, and reliability
• Backend load and stress conditions
• Web performance for critical flows

Partner with engineering teams to analyze production logs, crash reports, browser telemetry, and service metrics. Lead root-cause analysis of complex cross-layer defects spanning mobile UI, APIs, backend services, and web surfaces.

Ensure reliability validation is embedded directly into release workflows.

Cross-Functional Collaboration and Leadership

Collaborate closely with mobile engineers, backend developers, web engineers, product managers, DevOps teams, and release managers to define clear, testable requirements and release criteria. Actively participate in sprint grooming, planning, stand-ups, and retrospectives.

Influence best practices around mobile-first design, API contracts, and release readiness. Support mobile app release activities, including release candidate validation, go/no-go recommendations, and post-release monitoring.

Mentor junior QA engineers and contribute to raising the technical bar in automation and cross-platform validation. Work effectively with globally distributed teams to coordinate testing across time zones.

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xAI's mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge.

Our team is small and highly motivated, focused on engineering excellence. We operate with a flat organisational structure, where all employees are expected to be hands-on and contribute directly to the company's mission.

Responsibilities

We're looking for a Member of Technical Staff to join our team. Key responsibilities include:

Scaling synthetic coding data to trillions of tokens with large-scale Docker verification. Distilling the intelligence of flagship models into flash models through synthetic data generation. Optimising mid-training data mixtures to boost the ceiling for RL. Engineering long-context data recipes. Developing robust and diverse evaluation for mid-training checkpoints.

Basic Qualifications

To be successful in this role, you'll need:

Expertise in ML and large model scaling, with familiarity across all kinds of scaling laws. Strong ability to design ML experiments. Familiarity with state-of-the-art techniques for curating AI training data for text, image, audio, and video modalities. Strong engineering abilities in Spark, Ray, and other frameworks for large-scale data processing.

Compensation and Benefits

The base salary for this role is $180,000 - $440,000 USD. Our total rewards package includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

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At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life.

We are a global company with teams distributed between France, USA, UK, Germany, and Singapore. We offer a comprehensive AI platform that meets enterprise needs, whether on-premises or in cloud environments. Our offerings include le Chat, the AI assistant for life and work.

Role Summary

Mistral AI is seeking Applied Scientists Interns and Research Engineers Interns to drive innovative research and collaborate with clients on complex research projects. You will develop SOTA models across different modalities such as text, image, and speech. By developing novel methods and research ideas, you will apply these models across a diverse set of use cases and domains.

Responsibilities

• Run pre-training, post-training, and deploy state-of-the-art models on clusters with thousands of GPUs. • Generate and curate data for pre-training and post-training, working on evaluations and making sure the model's performance beats expectations. • Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation, and deployment. • Collaborate with cross-functional teams to tackle complex use cases using agents and RAG pipelines. • Manage research projects and communications with client research teams.

About You

• You are fluent in English, and have excellent communication skills. You are at ease explaining complex technical concepts to both technical and non-technical audiences. • You're an expert with PyTorch or JAX. • You're not afraid of contributing to a big codebase and can find yourself around independently with little guidance. • You write clean, readable, high-performance, fault-tolerant Python code. • You don't need roadmaps: you just do. You don't need a manager: you just ship. • Low-ego, collaborative, and eager to learn. • You have a track record of success through personal projects, professional projects, or in academia.

Benefits

• Competitive salary • Food: Daily lunch vouchers • Sport: Monthly contribution to a Gympass subscription • Transportation: Monthly contribution to a mobility pass

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As a Model Behavior Architect at Mistral AI, you will be at the forefront of defining and measuring Large Language Model (LLM) behavior. You will work closely with our Science team to define what 'good' looks like for various tasks, including Reasoning, Audio, Alignment, Tools, and Frontier bets.

Responsibilities

• Interact with models to identify areas for improvement in model behavior
• Gather internal and external feedback on model behavior to scope areas for improvement
• Design and implement evaluation pipelines, data guidelines, data generation, and synthetic testing environments
• Identify and fix edge case behaviors through rigorous testing
• Develop robust evaluation pipelines for model candidates
• Collaborate with AI Scientists

About you

• You have a deep understanding of linguistics, language, and translation, engineering and code behavior, or LLM agents at work, including reasoning and tool use
• You have prior knowledge in training and optimizing model behavior
• You are an expert at building robust evaluations
• You thrive in dynamic and technically complex environments
• You have a track record of delivering innovative, out-of-the-box solutions to address real-world constraints

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