Job description

SOFTWARE ENGINEER, BENCHMARKING (SF, NYC, OR REMOTE) ABOUT US Office Hours is an on-demand expert network that connects leading organizations with trusted experts across various knowledge domains. Experts earn income by sharing their knowledge through advisory work, projects, and AI model training. Our platform handles the complexities behind the scenes— screening, compliance, scheduling, and payments—so knowledge sharing stays focused on meaningful insights and real impact. We're a hyper-growth and profitable company, quickly expanding our expert network, launching new offices, and new products. We are headquartered in San Francisco, with offices in Brooklyn and Bangalore. Our customers include the fastest-growing digital health companies, technology companies, institutional investment firms, consulting firms and AI Labs. We are backed by top marketplace investors and operators of companies like DoorDash, Airbnb, Affirm. WHAT WE BELIEVE Human knowledge is the world's most valuable asset. And yet, despite being more interconnected than ever, most knowledge still remains stuck in our heads, inaccessible and underutilized. Our vision is to make human knowledge easily accessible and infinitely scalable by building tools for the new age knowledge economy. ABOUT THE ROLE We're looking for a Software Engineer to build and run the platform behind our AI model evaluations. You'll work closely with our research team to prepare benchmark datasets, build the pipelines and environments our evaluations run in, and turn results into published output. Our researchers design the methodology. You'll turn it into systems that run consistently and reproducibly, so results stay comparable across models, agent scaffolds, and time. Evaluations draw on the knowledge domains our expert network covers. WHAT YOU'LL DO - Prepare and maintain benchmark datasets: Own the data work behind our benchmarks, including cleaning, preparation, conversion into runnable formats, and ongoing maintenance. Validate that tasks are complete, consistent, and executable, and flag ambiguities that would compromise results. - Build and maintain evaluation pipelines: Build the infrastructure that runs evaluations consistently across model APIs and terminal agents, so results are reproducible and comparable. - Build evaluation environments: Create lightweight, containerized environments and viewers for tasking and for evaluating model performance on tool use. - Support model experiments: Help fine-tune small open-source LLMs and compare baseline against post-training performance. - Develop the scoreboard and leaderboard: Build the published views of our results, including model-level, benchmark-level, task-level, domain-level, and rubric-level performance. - Build analysis tools: Make it easy to identify recurring failure modes, compare models and agent scaffolds fairly, and track capability improvements and regressions over time. - Collaborate: Work closely with researchers and engineers to make sure evaluation data and outputs are accurate, consistent, and well integrated into what we publish. - Build tooling for data creation and review: Support expert annotation and data-generation projects by building lightweight HTML viewers and internal tools for task authoring, review, quality control, and structured data collection. WHAT YOU BRING - Solid engineering skills: 4+ years of professional experience building and maintaining complex systems, with strong Python. You write robust, maintainable code and are comfortable diving deep into existing codebases and infrastructure. - Data rigor: Experience preparing, cleaning, and maintaining datasets, and the care to make sure two results are genuinely comparable. - Comfort with containers and environments: Experience with Docker and building reproducible execution environments. - Collaborative: You work well alongside researchers and scientists and can translate their methodology into working systems. Hands-on experience running AI evaluations, or with frameworks like Harbor, Terminal-Bench, or Inspect, is a strong plus. TECH STACK - Evaluations: Python, model APIs, agent/evaluation frameworks, custom evaluation tooling - Models: APIs from the major AI providers, terminal agents, and open-source models via the Hugging Face ecosystem and PyTorch - Environments: Docker - Publishing: React, Next.js, Tailwind - Workflow: GitHub, Slack, Notion, Linear BONUS EXPERIENCE - Experience fine-tuning or post-training open-source LLMs, or other hands-on machine learning work - Experience with agentic, multi-turn, long-context, or tool-use evaluation - Experience validating LLM-as-judge or rubric-based grading setups - Background or strong interest in a scientific or technical domain - Experience building data-heavy dashboards, leaderboards, or visualizations - Open-source contributions or published work related to benchmarks and measurement BENEFITS + PERKS - Competitive salary and equity - Medical, dental, and vision coverage - 401(k) - Monthly wellness and fitness stipend - Paid time off policy, along with company holidays - Annual company off-sites (Tahoe, Mendocino, Mexico City, San Diego, Park City) - Flexible remote work policy - Paid parental leave (12 weeks of fully paid bonding leave, and birthing parents get 8–10 additional weeks of paid recovery leave) Pay Transparency Notice Full-time offers include base salary, equity, and benefits. Pay range: $160,000-$210,000, based on seniority, relevant experience and location This role can be fully remote or hybrid out of our SF or NYC offices. Don't meet every single requirement? Studies have shown that some candidates, especially underrepresented groups such as women and people of color, are less likely to apply to jobs unless they meet every single qualification. At Office Hours we believe in building a diverse and inclusive workplace, so if you're excited about this role but don't meet every qualification in the job description, we still encourage you to apply. You could still be the right candidate for this or other roles at Office Hours!

Requirements

Must-haves

SOFTWARE ENGINEER, BENCHMARKING (SF, NYC, OR REMOTE)
Build and maintain evaluation pipelines: Build the infrastructure that runs evaluations consistently across model APIs and terminal agents, so results are reproducible and comparable.
Build evaluation environments: Create lightweight, containerized environments and viewers for tasking and for evaluating model performance on tool use.
Support model experiments: Help fine-tune small open-source LLMs and compare baseline against post-training performance.
Develop the scoreboard and leaderboard: Build the published views of our results, including model-level, benchmark-level, task-level, domain-level, and rubric-level performance.
Build analysis tools: Make it easy to identify recurring failure modes, compare models and agent scaffolds fairly, and track capability improvements and regressions over time.
Collaborate: Work closely with researchers and engineers to make sure evaluation data and outputs are accurate, consistent, and well integrated into what we publish.
Data rigor: Experience preparing, cleaning, and maintaining datasets, and the care to make sure two results are genuinely comparable.
Comfort with containers and environments: Experience with Docker and building reproducible execution environments.
Collaborative: You work well alongside researchers and scientists and can translate their methodology into working systems.
Hands-on experience running AI evaluations, or with frameworks like Harbor, Terminal-Bench, or Inspect, is a strong plus.
Evaluations: Python, model APIs, agent/evaluation frameworks, custom evaluation tooling
Models: APIs from the major AI providers, terminal agents, and open-source models via the Hugging Face ecosystem and PyTorch
Environments: Docker
Publishing: React, Next.js, Tailwind
Workflow: GitHub, Slack, Notion, Linear
Experience fine-tuning or post-training open-source LLMs, or other hands-on machine learning work
Experience with agentic, multi-turn, long-context, or tool-use evaluation
Experience validating LLM-as-judge or rubric-based grading setups
Background or strong interest in a scientific or technical domain
Experience building data-heavy dashboards, leaderboards, or visualizations
Open-source contributions or published work related to benchmarks and measurement
Competitive salary and equity
Monthly wellness and fitness stipend

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythondockerreactnext.jsmachine learningllm

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Parental Leave

Wellness