Forward Deployed Engineer

Forward Deployed Engineer

Build AI that matters in the real world

What if your work could help prevent water loss across a city, strengthen healthcare delivery for millions of people, optimise critical energy infrastructure or turn Formula E telemetry into a better race strategy in the seconds that matter?

At Sand, Forward Deployed Engineers (FDEs) use AI, data and software to solve problems that have consequences far beyond a screen. In this role, you will work inside some of the world’s most complex operational environments, alongside the people who run them: turning messy data and ambitious ideas into solutions that deliver measurable results and improve people’s lives.

You won’t spend months building against a distant specification. You’ll get close to the problem, prototype quickly, deploy into production and see the impact of what you build.

If you want to apply advanced technology to meaningful problems; and work with exceptional colleagues who are as ambitious, curious and practical as you are, this could be the role for you.

About Sand

Sand Technologies is a global Physical AI company building intelligent systems for the infrastructure society depends on.

We partner with governments, cities and enterprises to improve how essential systems operate across healthcare, water, energy, telecommunications and infrastructure. Our teams have built AI systems that help manage London’s water supply for 16 million customers, supported telecommunications planning across hundreds of cities (and islands), and developed digital healthcare platforms serving millions of people across Africa.

Our work also takes us into less conventional environments. Through our partnership with Envision Racing, we have applied AI and data science to Formula E race analysis—helping engineers process complex performance data faster and improve decision-making during highly time-constrained race weekends.

Across these projects, the principle is the same: use AI to understand the physical world, make better decisions and improve outcomes that matter.

Our mission is to harness AI to solve humanity’s most pressing challenges.

Meet the team

Want to know what working here is really like? Meet the Sand team on YouTube and hear directly from the scientists, engineers and leaders behind our work. Click the names below to learn more on some key team members at Sand.

Dr. Mike Flaxman

Chief Science Officer

Nomcebo Ngwamba

Product Engineering Lead

Ross McIntosh

VP of R&D

Lizette Loubser

Technical Lead

About the role

FDEs are Sand’s technical front line within our most strategic customers.

Embedded in the client’s environment, you will work alongside operators, engineers, analysts and executives to understand high-value problems, unlock and structure data, rapidly test ideas and deploy production solutions. Depending on the engagement, you could be building data pipelines, predictive models, optimisers, AI agents, decision-support tools or operational applications.

This is not traditional consulting, and it is not conventional software engineering. It is an end-to-end value-engineering role combining deep technical execution, product thinking, client partnership and commercial awareness.

You will be trusted to navigate ambiguity, identify where AI can genuinely create value and turn that opportunity into something people use.

What makes an FDE different?

An FDE Data Engineer still builds pipelines, and an FDE Data Scientist still builds models. The difference is that they are so close to the problem, inside the client’s environment, owning the outcome from discovery through adoption.

Dimension

Forward Deployed Engineer

Traditional Engineer

Where you work

Embedded in the client environment, with up to 70% travel

Primarily within a central or remote engineering team

Proximity to the problem

Works directly with end users and sees the operational reality firsthand

Often receives requirements through a product manager or backlog

Scope

Discovers, scopes, builds, deploys and iterates across the full solution lifecycle

Typically owns a defined component of the build

Measures of success

The client’s problem is solved and measurable value is created

The assigned component ships to specification

Starting point

Ambiguous problems, fragmented data and evolving requirements

A more clearly defined brief and technical scope

Client interaction

Builds relationships and manages technical and operational stakeholders directly

Usually has limited customer interaction

Mindset

Engineer, problem-solver, product thinker and trusted adviser

Specialist operating primarily within one technical discipline

What you’ll do

Discover and prove value

• Embed within customer teams to understand their operations, constraints and highest-value problems.
• Translate complex operational challenges into clearly defined technical opportunities.
• Use rapid analysis, prototyping and iterative validation to prove value within weeks rather than months.
• Establish clear success measures and demonstrate tangible financial, operational or societal impact.

Build and deploy real solutions

• Design and deploy models, pipelines, dashboards, optimisers, AI agents and operational tools.
• Turn fragmented, imperfect real-world data into reliable products and actionable intelligence.
• Work through data-access, integration, security and workflow constraints without losing momentum.
• Build with a production mindset, balancing speed with reliability, maintainability and responsible use.

Drive adoption

• Work directly with operators, engineers and senior stakeholders to build trust and sustain momentum.
• Translate technical concepts into clear decisions and practical actions.
• Test solutions with users, incorporate feedback quickly and ensure the tools you build become part of day-to-day operations.
• Train client teams and transfer knowledge so solutions continue creating value after deployment.

Shape Sand’s platform

• Capture reusable patterns, components and workflows from field deployments.
• Work closely with Product and Platform Engineering to turn customer learning into stronger platform capabilities.
• Surface recurring technical gaps and help prioritise the product roadmap.
• Ensure each deployment makes the next one faster, stronger and more scalable.

Contribute to customer growth

• Recognise where successful work could solve additional client problems or create value at greater scale.
• Bring field intelligence into the development of new enterprise AI propositions.
• Support the shaping and technical scoping of follow-on projects.
• Help turn strong initial delivery into long-term strategic partnerships.

Who you are

You are likely to thrive in this role if you:

• Have 3–5+ years of experience as a Data Scientist, Data Engineer, ML Engineer, Software Engineer or in a similarly technical role.
• Have built solutions using complex, imperfect, real-world datasets.
• Can move confidently between understanding a problem, writing code, testing with users and deploying a working solution.
• Have experience working directly with customers, operators or business stakeholders.
• Communicate clearly with both highly technical and non-technical audiences.
• Are comfortable making progress without a perfect brief or established playbook.
• Take ownership of outcomes and remain resourceful when you encounter constraints.
• Want your technical work to create visible, meaningful impact in the physical world.
• Are excited by significant travel and spending time embedded with customers.

Technical experience

We’re looking for strong fundamentals rather than one exact technology stack. Relevant experience may include:

• Strong programming ability, preferably in Python, together with SQL and modern data tooling.
• Preference for front-end product engineering capabilities (including Typescript).
• Data ingestion, transformation and pipeline development.
• APIs, cloud environments and enterprise data architectures.
• Applied machine learning, optimisation, simulation or operations research.
• Modern AI development, including LLM applications, agents, retrieval and evaluation.
• Data visualisation and rapid application or prototyping frameworks.
• Deploying, monitoring and iterating production solutions.

Experience in energy, utilities, healthcare, telecommunications, government or another asset-intensive industry is valuable, but not essential.

Selection process

As part of our interview process, you'll spend time with us in person in New York — working alongside our team on a real, hands-on problem, the same way an FDE operates in the field. No slide decks, no hypotheticals: you'll build, adapt under pressure, and present your solution the way you would to a client whose operations depend on it.

How we work

At Sand, we own outcomes, not simply tasks or functions.

We bring together engineers, scientists, product builders and industry experts from across the world to solve problems that rarely fit neatly within one discipline. You may begin a week inside a customer’s operational environment, spend the next few days building and testing a prototype, and finish presenting the results to senior decision-makers or working with our platform team to turn your approach into a reusable capability.

You will have considerable autonomy, but you will not work alone. You’ll be surrounded by deeply capable colleagues who share context freely, challenge one another thoughtfully and step across conventional role boundaries when the problem requires it.

We value people who are curious enough to ask better questions, practical enough to ship, and ambitious enough to believe AI can (and should) make the systems people rely on work better.