About the role

As a Forward Deployed Engineer Intern, you will work alongside a senior FDE on real client engagements with leading AI labs. You will own concrete pieces of those engagements - writing scripts to process and analyze data, building automated quality checks, and helping validate that the data we deliver is good enough to ship. The work is hands-on, fast-paced, and rarely arrives with a complete spec.

This is not a shadowing internship. You will be expected to take ownership of your projects, work through ambiguity with your manager, and ship work that real clients depend on. If you do well, you will see your work translate directly into how frontier AI models are trained and evaluated.

About Labelbox

Labelbox builds the data infrastructure behind frontier AI development. We work with leading research labs and enterprises to produce the high-quality datasets used for evaluation, supervised fine-tuning, and reinforcement learning. The Frontier AI team you will be joining sits at the intersection of engineering and applied research, working directly with AI lab partners on some of the most demanding data problems in the industry.

What you'll do

• Work alongside a senior FDE on live client engagements, owning specific workstreams end-to-end.
• Write scripts to process, transform, and analyze data for client deliverables.
• Build automated quality checks and serve as a second set of eyes on the data we ship.
• Take ambiguous, partially-defined requirements and turn them into something concrete.
• Read existing code, extend it, and improve it - most of what you build will plug into systems that already exist.
• Drive your work forward without needing daily check-ins. Proactively share progress, flag blockers, and ask for input when you need it.

What we're looking for

We care more about how you think than about a specific list of credentials. The things below are what tend to make interns successful in this role:

• Strong Python skills, especially for data work. You should be comfortable picking up a messy dataset and figuring out what is in it.
• Familiarity with LLMs from hands-on experience, not just coursework. You have built something with them and have iterated on prompts to make them do what you want.
• Strong analytical thinking. You can look at data, spot what is off, and ask the right questions about whether a result is real or noise.
• Strong written and verbal communication. You can write a clear update, explain technical work to non-technical stakeholders, and ask a well-formed question when you are stuck.
• You finish what you start. When you take on a task, you see it through to the end - including the parts that are not interesting.
• Fast learner. The tools, frameworks, and benchmarks you will encounter change quickly. You pick them up without being walked through them.
• High agency and ownership. You take responsibility for your work, push it forward without being prompted, and flag issues early rather than late.

Who you are

• Currently pursuing a Bachelor's or Master's in Computer Science, Engineering, Mathematics, or a related technical field.
• Genuinely curious about AI and how frontier models are built.
• You like being the person who turns “we should look into this” into something concrete.
• You work independently and communicate generously. You move fast on your own.
• Receptive to feedback.

Why this role

FDE work at Labelbox is a spot where it spans engineering, research, and direct client work. As an intern, you will get exposure to all three - and to the kinds of problems that most engineering internships do not touch. You will work with people who care about getting the technical details right and who will give you the room to do the same.

Compensation

This is a paid internship with an hourly rate of $50-$70 per hour.

The role

We’re hiring a Forward Deployed Engineering Manager to lead the design, development, and delivery of reinforcement learning environments for agentic AI systems.

You’ll manage a team responsible for building sandboxed, reproducible environments—terminal-based workflows, browser automation, and computer-use simulations—that power both model training and human-in-the-loop evaluation. This is a hands-on leadership role where you’ll set technical direction, guide execution, and stay close to architecture and critical systems.

What You’ll Do

• Lead, hire, and develop a high-performing team of Forward Deployed Engineers, setting a high bar for ownership, velocity, and technical quality
• Own the RL environment roadmap, aligning team execution with customer needs and evolving model capabilities
• Oversee development of sandboxed environments (terminal, browser, tool-augmented workspaces) that support deterministic execution and multi-step agent interaction
• Ensure reliability, observability, and data integrity through strong instrumentation (logging, trajectory capture, state snapshotting)
• Drive infrastructure excellence across containerization, sandboxing, CI/CD, automated testing, and monitoring
• Partner cross-functionally with data operations, product, and leading AI labs to define task design, evaluation protocols, and environment requirements
• Enable rapid prototyping and iteration, helping the team move from ambiguous requirements to production-ready systems quickly
• Stay close to the technical details—reviewing architecture, unblocking complex issues, and guiding design decisions

What We’re Looking For

Required

• 5+ years of software engineering experience (Python)
• 2+ years of experience managing or leading engineers in fast-paced environments
• Strong experience with containerization and sandboxing (Docker, Firecracker, or similar)
• Solid understanding of reinforcement learning fundamentals (MDPs, reward design, episode structure, observation/action spaces)
• Background in infrastructure, developer tooling, or distributed systems
• Strong debugging skills and systems thinking across layered, containerized environments
• Ability to operate in ambiguity and translate loosely defined problems into clear execution plans
• Excellent communication and stakeholder management skills

Preferred

• Experience building or working with RL environments (Gym, PettingZoo) or agent benchmarks (SWE-bench, WebArena, OSWorld, TerminalBench)
• Familiarity with cloud infrastructure (GCP or AWS)
• Prior experience in AI/ML platforms, data companies, or research environments
• Contributions to open-source projects in RL, agents, or developer tooling

Why This Role Matters

RL environment quality is a critical bottleneck in advancing agentic AI. Poorly designed or unreliable environments introduce noise into training loops and directly impact model performance.

In this role, you’ll lead the team building the environments that define how models learn—working across a range of cutting-edge projects with leading AI labs. Alignerr offers the speed and ownership of a startup with the scale and resources of Labelbox, giving you the opportunity to have outsized impact on the future of AI.

About Alignerr

Alignerr is Labelbox’s human data organization, powering next-generation AI through high-quality training data, reinforcement learning environments, and evaluation systems. We partner directly with leading AI labs to build the data and infrastructure that push model capabilities forward.

Role Overview

Alignerr is Labelbox's human data organization — we produce the training data that frontier AI labs use to build their most capable models. Our Forward Deployed Research Team sits at the intersection of research science and client delivery, embedding research capability directly into the engagements that drive our business.

This is not a traditional research scientist role. You will not spend months pursuing a single research question. You will work on multiple client engagements simultaneously, operating on timescales of days to weeks. You will sit in scoping meetings with research teams at major AI labs, reason scientifically about data strategy in real time, fine-tune open-weight models to validate our data methodology, and collaborate with our Applied Research team to turn client-grounded findings into published work. The pace is fast, the problems are applied, and the feedback loops are short.

We are looking for someone who finds that energizing, not compromising.

Your Impact

• Engage directly with frontier lab research teams. You will be in the room during client scoping meetings — not as support staff, but as a technical peer. You'll engage on methodology, challenge assumptions about data requirements, and shape project specifications based on a scientific understanding of how data composition affects model outcomes.

  Develop deep scientific understanding of client engagements. For each project, you will build a working model of the client's architecture, training methodology, and target capabilities. You'll use this understanding to reason about why a particular data strategy will or won't work, identify risks early, and iterate with empirical grounding — not intuition.

  Run ablation studies and fine-tune open-weight models. You will fine-tune models on client data (and proxy data) to empirically measure the impact of our data on model performance. This is how we validate that what we deliver actually improves our customers' models — and how we catch problems before the client does.

  Consult on workflow and quality systems. You will partner with our Human Data Operations team to review annotation schemas, task designs, and quality rubrics before projects go into execution. Your job is to ensure the spec is technically sound — that the data we produce will actually serve the client's training objectives.

  Collaborate with Applied Research on publications and benchmarks. Our Applied Research team owns the long-horizon research agenda. Your role is to feed them signal from the field — generalizable findings, reusable methodologies, empirical results — and help drive joint projects to completion. You will contribute to benchmarks, white papers, and conference submissions that establish Labelbox's research credibility.

What You Bring

• Required

  • MS or PhD in Machine Learning, NLP, Computer Science, or a related quantitative field.
  • Hands-on experience fine-tuning large language models (open-weight models such as Llama, Mistral, Qwen, or similar).
  • Strong understanding of LLM training pipelines — pretraining, supervised fine-tuning, RLHF/DPO, and how data quality and composition affect each stage.
  • Experience designing and executing experiments with rigor — hypothesis formation, controlled comparisons, statistical analysis of results.
  • Ability to operate at speed. You should be comfortable going from problem definition to experimental results in days, not months.
  • Strong written and verbal communication. You will present findings to client research teams and contribute to published work.

  Strongly Preferred

  • Prior experience at a frontier AI lab, applied ML startup, or in a research role with direct client/stakeholder interaction.
  • Experience with evaluation and benchmarking of LLMs — designing metrics, building eval harnesses, interpreting results critically.
  • Familiarity with human data pipelines — annotation workflows, quality assurance methodology, inter-annotator agreement analysis.
  • Experience with reinforcement learning, reward modeling, or RLHF environments.
  • Published research (conferences, journals, or technical reports) in ML/NLP or adjacent fields.

  What Matters More Than Credentials

  • Applied instinct over academic purity. The measure of success here is client impact and publishable-but-practical results — not methodological novelty for its own sake. If your first instinct when handed a problem is to build a framework, this isn't the role. If your first instinct is to run an experiment and get a result, it is.
  • Comfort with ambiguity and incomplete information. Client engagements rarely come with clean problem statements. You'll need to extract the real question from a noisy conversation, scope an approach quickly, and iterate.
  • Cross-functional fluency. You will work daily with field engineers, project managers, operations teams, and an independent Applied Research team. Someone who can only operate within a pure research silo will struggle here.
  • Intellectual honesty. When an ablation study shows the data isn't working, you need to say so — clearly and constructively — even when it's inconvenient for the deal timeline.

What You Should Know About This Team

• We are small and high-leverage. The FDRT is a team of five today. Every person's work directly influences client outcomes and Labelbox's market position.
• We operate at the tempo of client delivery. Two-week sprints. SLAs measured in days. If you want months of uninterrupted focus on a single problem, our Applied Research team is a better fit.
• We are at the intersection of several teams. FDRT works with Field Delivery Engineers, Human Data Operations, Applied Research, and client research teams. The role requires navigating those interfaces with credibility and without ego.
• We protect time for research. 25–30% of team capacity is allocated to research collaboration with Applied Research. This is not aspirational — it is a structural commitment. You will have the opportunity to publish.

The Role

We’re hiring a Forward Deployed Engineer to own the design, development, and operationalization of reinforcement learning environments. You’ll build the sandboxed, reproducible execution environments that AI agents interact with during training and evaluation—things like terminal-based task benchmarks, browser and computer-use environments, and tool-augmented agentic workspaces.

This is a hands-on engineering role. You’ll write production-quality infrastructure code, integrate with open-source RL tooling, and work closely with our data operations team to ensure environments are robust, observable, and ready for human annotators and model agents alike. You won’t be doing ML research, but you’ll need to deeply understand how RL training loops consume environments and where the bottlenecks live.

What You’ll Do

• Design, build, and maintain sandboxed RL environments for agentic AI training—including terminal emulators, browser automation harnesses, computer-use simulators, and tool-augmented workspaces (e.g., environments built on frameworks like TerminalBench, OSWorld, and Tau-bench)
• Develop reproducible, containerized execution environments (Docker, VMs, lightweight sandboxes) that support deterministic task rollouts and reward signal collection
• Integrate with and extend open-source agentic tooling and custom CLI/API harnesses to enable multi-step agent interaction
• Build instrumentation and observability layers—structured logging, trajectory capture, state snapshotting—so training runs and human annotation sessions produce clean, auditable data
• Collaborate with data operations to design task curricula and evaluation protocols that stress-test model capabilities across environment types
• Own environment deployment and reliability: CI/CD pipelines, automated testing of environment configurations, and monitoring for drift or breakage across versions
• Rapidly prototype new environment types as client and internal requirements evolve, moving from spec to working system in days, not weeks

What We’re Looking For

Required

• 2+ years of professional software engineering experience, with strong fundamentals in Python and at least one systems-level language (Go, Rust, C++)
• Demonstrated experience with containerization and sandboxing (Docker, Podman, Firecracker, or similar) in production or near-production contexts
• Familiarity with RL concepts: MDPs, reward shaping, episode structure, observation/action spaces. You don’t need to have trained models, but you need to understand what an environment must provide to an RL training loop
• Experience building or maintaining developer tooling, CLI tools, or infrastructure automation
• Comfort working with browser automation frameworks or terminal interaction tooling
• Strong debugging instincts—you can trace failures across process boundaries, container layers, and network calls
• Ability to read and implement from academic papers and open-source benchmark repositories without extensive hand-holding

Preferred

• Direct experience building or contributing to RL environments (Gymnasium/Gym, PettingZoo, or custom environment implementations)
• Experience with agentic AI evaluation frameworks (SWE-bench, WebArena, OSWorld, TerminalBench, or similar)
• Familiarity with GCP or AWS infrastructure (Compute Engine, ECS/EKS, Cloud Build)
• Prior work at an AI data company, ML platform company, or AI research lab
• Contributions to open-source projects in the RL, agents, or dev-tools space

Candidate Archetype

The ideal candidate is a strong software engineer first, with genuine curiosity and working knowledge of reinforcement learning. You’ve probably built infrastructure or developer tooling at a startup or mid-stage company, and you’ve been pulled toward the ML/AI space—maybe through side projects, open-source contributions, or a prior role adjacent to an ML team. You’re the kind of engineer who reads an RL benchmark paper and immediately thinks about how to make the environment more robust, not how to improve the policy gradient.

You thrive in ambiguity. You can take a loosely defined project requirement—“build an environment that tests an agent’s ability to navigate a file system and execute multi-step bash workflows”—and deliver a working, tested, documented system without needing a detailed spec. You move fast, but you care about reliability because you know environments that break silently poison training data.

Why This Role Matters

• RL environment quality is one of the biggest bottlenecks in agentic AI training today. Environments that are brittle, non-deterministic, or poorly instrumented produce noisy reward signals that directly degrade model performance. You’ll be solving one of the highest-leverage infrastructure problems in AI.
• You’ll work across a portfolio of projects spanning different AI labs and model capabilities—no single-product monotony. The environment types you build will evolve as the frontier of agent capabilities moves.
• Alignerr is a small, high-impact team inside a well-funded company (Labelbox). You’ll have startup-level ownership with growth-stage resources.

_{Alignerr Services at Labelbox}

Alignerr is Labelbox’s human data organization, purpose-built to generate the high-quality training data that powers the next generation of AI models. We partner directly with leading AI labs to produce reinforcement learning environments, evaluation benchmarks, and expert-annotated datasets that push model capabilities forward. Our team sits at the intersection of software engineering, ML infrastructure, and human-in-the-loop data production.

Role Overview

Get an inside look and hear directly from our current Forward Deployed Engineers here!

This role requires an entrepreneurial mindset. You will operate like a technical cofounder: with high agency, deep technical engagement, and a bias for action.

You will use a unique mix of engineering, product, and sales to deliver data on high stakes projects for leading frontier AI labs. You will work with human data teams or AI researchers in customer organizations to produce the best datasets for model evals, supervised finetuning, or ground truth data for advanced and emerging reinforcement learning techniques.

Your Impact

• Understand the Data Needs of AI Leaders: Work directly with the most advanced AI labs to define and refine data strategies.

• Design and Operate Human Data Pipelines: Build scalable, high-quality data pipelines to power next-gen AI.

• Develop and Optimize Code: Write Python scripts for data processing and quality analysis.

• Shape the Future of AI Infrastructure: Define engineering requirements to improve human data tools and workflows.

What You Bring

• Master’s degree or higher in Computer Science, Engineering, Mathematics, or AI-related fields.

• 2+ years of professional experience in a related field.

• Proficiency in Python and data analysis.

• Prior experience leading LLM projects.

• Exceptional communication skills: ability to convey complex technical concepts clearly.

• Strong project management and organizational skills.

• Passion for AI and the intersection of technology, product, and customer needs.

Alignerr Services at Labelbox

As part of the Alignerr Services team, you'll lead implementation of customer projects and manage our elite network of AI experts who deliver high-quality human feedback crucial for AI advancement. Your team will oversee 250,000+ monthly hours of specialized work across RLHF, complex reasoning, and multimodal AI projects, resulting in quality improvements for Frontier AI Labs. You'll leverage our AI-powered talent acquisition system and exclusive access to 16M+ specialized professionals to rapidly build and deploy expert teams that help customers, which include the majority of leading AI labs and AI disruptors, achieve breakthrough AI capabilities through precisely aligned human data—directly contributing to the critical human element in advancing artificial intelligence.