Product Engineer, Encord
DaaS
$150-250k
+ Equity + Bonus
4 days a week in office
Data platform for AI training
Be an early applicant
Data platform for AI training
201-500 employees
Be an early applicant
$150-250k
+ Equity + Bonus
4 days a week in office
201-500 employees
Company mission
To solve AI alignment by tackling the real bottleneck: data quality.
Role
Who you are
- Are a builder: You know the difference between having a job and building something. You've founded something, been engineer #1, or been the engineer PMs quietly route the ambiguous stuff to
- Are experienced: You've personally built and maintained systems from scratch, and you know what trade-offs are involved in shipping reliable, performant software at speed
- Want customers in the room: Discovery calls, feedback, iteration: you'd rather hear it first-hand than read it in a ticket
- Are a team player: You raise the people around you. You contribute your best work and help others contribute theirs
What the job involves
- We're looking for a Product Engineer to join our Data as a Service team in San Francisco
- LLMs happened because internet scale text already existed. Robotics has no internet to scrape. Nobody has labelled the physical world at scale, and every robot, AV, and surgical system on earth is stuck on exactly that. Surgical teams need thousands of labelled procedures. AV teams are sitting on 100,000 hours of footage they can't use
- Our Data as a Service team exists to unstick all of it. Customers hand us robotics video, LiDAR, medical imaging. We hand back labels. Sounds simple. It isn't: an ML layer routing work between models and expert humans, quality that's measured rather than vibes, one platform serving AI labs on one side and a global annotation workforce on the other
- The teams work is largely greenfield. There's no legacy system to work around and no playbook to follow, because nobody has built this before. What you ship becomes the foundation. The last wave of AI was built on internet data; the next one will be built on data from the physical world, and this team is building the machine that produces it
- No ticket queue, no PM between you and the user: Product engineering here means the real version. You talk to the customer, figure out what matters, make the product call, ship it, and own the outcome: the win and the blast radius
- Genuinely unsolved problems: Design the workflow where human reviewers and AI models hand work back and forth thousands of times an hour, and it feels seamless to the customer. Build one product that flexes across robotics, AV, and healthcare without turning into three products
- End-to-end ownership: From product, design, and architectural decisions, all the way to deployment, monitoring, and measuring impact on users
- Autonomy and collaboration: You will often work autonomously to take charge of your projects and drive your work forward. But as a lean and high trust team, we remain quick to collaborate and support each other with challenging problems together
Our take
It has become increasingly clear that the next generation of AI applications will be driven by data-centric approaches concentrated around training data creation, management, and evaluation. Crucial within this process is the annotation or 'labelling' of data. Traditionally, such annotation has been left to human workers, which has historically made this part of the process of AI development expensive, slow, and difficult to scale.
Encord offers a computer vision annotation platform that automates a number of manual labelling tasks. Its suite of tools is designed for collaboration across roles and teams, from domain-expert annotators to project managers and machine learning engineers. This allows companies to iterate 10x faster and develop and deploy high-performing models in less time whilst saving on annotation costs.
Demand is clearly there: since its launch in 2020, Encord has facilitated the annotation of more than 250 million frames and images and served customers in verticals such as medical imaging, smart cities, sports analytics and satellite imaging. It was awarded $44.3 million in Series C funding in 2026, which will help it scale and meet demand for its AI-native data infrastructure. Indeed, usage is scaling rapidly, with the number of labels generated on the platform growing ~70% month-over-month.
Steph
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 5 rounds)
Feb 2026
$60m
SERIES C
Aug 2024
$30m
SERIES B
Total funding: $107.2m
Company benefits
- Competitive salary, commission, and meaningful equity in a high-growth startup
- Strong in-person culture: 3–5 days/week in our London, SF, and NYC offices
- Flexible PTO to fully recharge
- Annual learning & development budget
- Frequent travel opportunities across the U.S., London, and Europe
- Bi-annual company offsites, twice-weekly team lunches, and monthly socials
- Company lunches twice a week
- Clear, accelerated growth opportunities as the company scales rapidly
- Comprehensive health, dental, and vision coverage
Company HQ
North Beach, San Francisco
Articles
Leadership
Eric Landau
(Co-Founder & Co-CEO)With MScs from Harvard and Stanford, Eric spent 8+ years as a senior quant at DRW deploying ML at scale—before co-founding Encord to make data the core of AI development.
Ulrik Stig Hansen
(Co-Founder & Co-CEO)Ulrik spent 3+ years in Sales & Trading at J.P. Morgan and holds science master’s degrees from UCL and Imperial—before co-founding Encord to transform how AI handles data.
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