- Страна
- США
- Зарплата
- 145 000 $ – 200 000 $
Откликайтесь
на вакансии с ИИ

Embodied AI / Simulation Engineer
Отличное предложение в быстрорастущем стартапе с серьезным финансированием и реальным продуктом. Высокая зарплата, опционы и работа с передовыми технологиями (humanoid robots) делают эту вакансию крайне привлекательной для специалистов по ИИ.
Сложность вакансии
Роль требует редкого сочетания навыков в области глубокого обучения (Transformer, Diffusion models) и практической робототехники (Isaac Sim, sim-to-real). Высокая сложность обусловлена необходимостью работы с физическим оборудованием и строгими требованиями к надежности систем в реальном времени.
Анализ зарплаты
Предлагаемый диапазон $145k – $200k полностью соответствует рыночным стандартам для Senior/Staff инженеров в области ИИ и робототехники в районе залива Сан-Франциско. Верхняя граница диапазона является конкурентной для стартапов стадии роста.
Сопроводительное письмо
I am writing to express my strong interest in the Embodied AI / Simulation Engineer position at Formic. With a deep background in training learning-based manipulation policies and a proven track record of successful sim-to-real transfer, I am excited by Formic's mission to make automation accessible through the Robotics-as-a-Service model. My experience with transformer-based action models like ACT and diffusion policies aligns perfectly with your technical stack and the challenges of deploying robust visuomotor policies in production environments.
In my previous work, I have focused on building high-fidelity simulation environments using Isaac Sim and MuJoCo to bridge the gap between digital twins and physical hardware. I am particularly impressed by Formic's commitment to operational efficiency and the "Today, Not Tomorrow" philosophy. I am eager to bring my expertise in PyTorch and real-time control stacks to the Software Engineering Team in Oakland to help scale your fleet of humanoid and mobile manipulation platforms.
Составьте идеальное письмо к вакансии с ИИ-агентом

Откликнитесь в formic уже сейчас
Присоединяйтесь к Formic, чтобы внедрять передовые алгоритмы ИИ в реальное производство и менять облик американской промышленности!
Описание вакансии
Who we are:
Formic is on a mission to reshape American manufacturing by making automation accessible to every factory. As labor constraints rise, costs increase, and global competition intensifies, automation is no longer optional for manufacturers that want to stay competitive.
We deliver automation through a Robotics-as-a-Service model that combines industrial robotics, proprietary software, and full-service support into a single, integrated solution. By removing the traditional barriers of cost, complexity, and risk, we enable manufacturers to deploy automation quickly and realize measurable gains in throughput, safety, and operational efficiency without large upfront capital investment.
Backed by leading investors including Lux Capital, Initialized Capital, Blackhorn Ventures, and Mitsubishi HC Capital North America, Formic is scaling rapidly and building the foundation for a new era of high-performance, Made in America production.
About the team:
The Software Engineering Team builds and operates the systems that power Formic’s Robotics-as-a-Service platform.
Engineering focuses on ensuring deployed systems are observable, resilient, and remotely diagnosable at scale. The team builds production-grade edge and cloud systems that support reliable data collection, remote troubleshooting, live video streaming, and continuous system improvement.
This work directly impacts fleet uptime, service efficiency, and customer outcomes by ensuring Formic’s monitoring and control infrastructure remains scalable, reliable, and continuously evolving.
About this role:
As an Embodied AI / Simulation Engineer, you will develop learning-based manipulation systems for humanoid and mobile manipulation platforms and ensure they transfer reliably into the real world. You will build the simulation, data, and training infrastructure needed to develop robust visuomotor policies and deploy them onto physical robotic systems operating in production environments.
This role sits at the intersection of simulation, machine learning, and robotics execution. You will work closely with perception and controls teams to ensure learned policies operate safely, reliably, and effectively in closed-loop real-world conditions.
In this role you will:
- Design, train, and evaluate learning-based manipulation policies for humanoid and mobile manipulation platforms
- Develop and maintain high-fidelity simulation environments and digital twins using Isaac Sim, MuJoCo, or similar tools
- Implement and benchmark approaches including Action Chunking with Transformers (ACT), diffusion policies, behavior cloning, and Vision-Language-Action (VLA) models
- Contribute to a Universal Manipulation Interface (UMI) abstraction layer that standardizes policy inputs, outputs, and deployment interfaces
- Build scalable teleoperation-to-training data pipelines, including tooling for dataset generation, curation, and labeling
- Design and execute sim-to-real transfer strategies including domain randomization, system identification, and calibration workflows
- Analyze policy robustness, failure modes, safety constraints, and cross-task generalization in closed-loop settings
- Partner with perception and controls teams to integrate learned policies with real-time systems and ensure stable execution
- Deploy trained models onto physical robots and lead on-hardware validation, debugging, and iteration
- Establish experimental rigor through structured evaluation plans, ablations, reproducible training runs, and performance tracking
What makes you a great fit:
- Demonstrated experience training embodied AI policies that run on real robotic systems
- Strong understanding of sim-to-real transfer challenges, hardware constraints, and real-world failure modes
- Familiarity with transformer-based action models such as ACT
- Experience with diffusion policies or other generative approaches for control and decision-making
- Experience working with multimodal inputs including vision, proprioception, and language
- Proficiency in Python and deep learning frameworks such as PyTorch or JAX
- Experience integrating learned policies with real-time control stacks and deployment environments
- Strong experimental design and evaluation discipline, with attention to reproducibility and measurement quality
- Ability to operate effectively across research and production constraints while maintaining technical rigor
- Based in or willing to relocate to the Greater San Francisco Bay Area and able to work 5 days per week from Formic’s Oakland, CA office
Our Total Rewards:
At Formic, we believe people do their best work when they feel supported both professionally and personally. That’s why we offer a comprehensive benefits and perks package for full-time, U.S.-based team members, including:
- Equity in Formic: Participate in our stock option program and share in the success of a fast-growing start-up backed by leading global investors
- Competitive & Uncapped Commission Structure: Designed to reward performance and impact in commission-eligible roles
- Comprehensive Healthcare Coverage: Medical, dental, and vision insurance through Blue Cross Blue Shield and Unum, with 99% of employee premiums covered and 75% coverage for dependents, with optional buy-up plans available
- Additional Insurance Benefits: FSA and DCFSA, life insurance, short-term disability, and long-term disability through Unum, all 100% employer-paid
- Employee Assistance Program (EAP): Fully funded by Formic, offering support when you need it most
- Paid Parental Leave Program: Up to 12 weeks of paid parental leave
- Company-sponsored 401(k): Invest in your future with our company-facilitated retirement savings plan
- Home Office Stipend: A one-time allowance for fully remote and hybrid employees to support an at-home or on-the-road work setup
- Monthly Cell Phone Reimbursement: Monthly stipend toward personal phone and internet expenses
- Flexible Time Off: Take the time you need, when you need it, supported by our flexible PTO policy
Compensation Philosophy
Formic’s pay and equity packages are thoughtfully benchmarked against peer companies at a similar growth stage. Equity represents a meaningful part of our mutual investment: when Formic succeeds, so do you.
Final offers are customized based on experience, geographic location, market considerations, and a candidate’s preferred balance of cash and equity. Our goal is to attract and reward top talent who will have significant impact, and we are open to thoughtful discussions to align on the right structure.
Compensation Range:
$145,000—$200,000 USD
What we look for:
We’re building this company from the ground up, and every person we hire has an outsized impact on our culture, performance, and trajectory. While each team member brings unique strengths and perspectives, we look for people who align with our Operating Principles and embody them in action. If this sounds like you, Formic may be the right place for you!
- Fearless Optimism: You make bold bets and default optimistic. You believe in the mission, aren’t paralyzed by risk, and fear inaction more than failure. You see ambiguity as opportunity and bring energy to building what doesn’t yet exist.
- Create the Magic: You absorb complexity so customers don’t have to. Whether your customer is external or internal, you focus on delivering experiences that are clear, fast, value-added, and outcome-driven. You don’t say “not my job.” You make things work.
- Today, Not Tomorrow: You have a bias to action. You close the loop. You take extreme ownership. You understand that speed compounds, and you don’t confuse activity with results.
- Seek Truth: You think from first principles. You value data over ego and strong opinions loosely held. You’re willing to challenge assumptions, including your own, in pursuit of the best answer.
- Made of Rubber: You are resolute and adaptable. When things break or priorities shift, you rebound stronger. You treat setbacks as learning moments and move forward with grit.
- One Formic: You operate without silos. You practice radical helpfulness, document clearly, and make clean handoffs. You assume positive intent and prioritize team success over individual credit.
Equal Opportunity Employment:
Formic is an equal opportunity employer. We do not discriminate on the basis of race, color, religion or religious creed, sexual orientation, gender, gender identity, marital status, family or parental status, disability, military or veteran status, or any other basis protected by law. All employment decisions are based on a person’s merit, business needs, and role requirements. If you require further accommodations or have questions regarding accessibility of our roles, please reach out to careers@formic.co.
AI Use:
At Formic, fairness and transparency are at the heart of our hiring process. We use AI-powered tools in some interviews to help our teams evaluate candidate responses, but all final hiring decisions are made by humans. You can learn more about how AI is used in our recruitment process by reviewing our AI Hiring Disclosure linked here.
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- Python
- PyTorch
- JAX
- Isaac Sim
- MuJoCo
- Robotics
- Computer Vision
- Deep Learning
- Reinforcement Learning
- Transformer
- Diffusion Models
- Behavior Cloning
- Sim-to-Real
Возможные вопросы на собеседовании
Проверка понимания ключевой проблемы переноса обученных моделей из симуляции на реальное железо.
Расскажите о вашем опыте преодоления разрыва между симуляцией и реальностью (sim-to-real). Какие методы рандомизации доменов или идентификации систем вы считаете наиболее эффективными?
Вакансия требует опыта работы с современными архитектурами для управления роботами.
В чем, по вашему мнению, основные преимущества и недостатки использования Action Chunking with Transformers (ACT) по сравнению с диффузионными политиками для задач манипуляции?
Работа подразумевает создание пайплайнов данных для обучения.
Как бы вы спроектировали масштабируемую систему сбора и разметки данных телеопераций для обучения политики поведения (behavior cloning)?
Роль требует интеграции ИИ с низкоуровневым управлением.
Опишите ваш опыт интеграции нейросетевых политик с системами управления реального времени. Как вы обеспечиваете безопасность и стабильность выполнения команд?
Проверка навыков отладки сложных систем.
Как вы подходите к анализу причин сбоев (root cause analysis), когда обученная политика ведет себя некорректно на физическом роботе, несмотря на успех в симуляции?
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- Страна
- США
- Зарплата
- 145 000 $ – 200 000 $