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Cognite

Senior Field Engineer (Pre-Sales/Forward Deployed)

AI Summary
This senior field engineering role combines pre-sales solution engineering, forward-deployed development, and industrial AI architecture. The engineer partners with account executives and customers to convert strategic use cases into working prototypes built with Cognite Atlas AI and Cognite Data Fusion. Responsibilities include designing agentic AI solutions, integrating legacy IT/OT systems, running technical workshops, and presenting value to executive and engineering audiences. The role requires strong Python and GenAI expertise, customer-facing communication, and up to 50% travel. Industrial domain knowledge in sectors such as energy, oil and gas, chemicals, or life sciences is strongly preferred.
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Remote from
USA
Salary
Undisclosed
Department
Software Engineering
Employment
Full Time
Experience
Director
Published
Apply before
13 Dec 2026
Listing views
28
Application actions
1
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Opportunity details

About this role.

What Cognite is: Relentless to achieve

Cognite operates at the forefront of industrial digitalization, building AI, and data solutions that solve the world’s hardest, highest-impact problems. With unmatched industrial heritage and a comprehensive suite of AI capabilities, including low-code AI agents, Cognite accelerates the digital transformation to drive operational improvements.

We thrive in challenges. We challenge assumptions. We execute with speed and ownership. If you view obstacles as signals to step forward – not backwards – you’ll feel right at home here.

Our Moonshot is bold: Unlock $100B in customer value by 2035, and redefine how global industry works. Join us in this venture where AI and data meet ingenuity, and together, we will forge the path to a smarter, more connected industrial future.

How you’ll demonstrate Ownership

  • Client Engagement & Moving Strategy to Reality: Partner with Account Executives to prioritize strategic opportunities and act as the technical lead for complex engagements. You will move clients from high-level strategy to live, functioning versions of Cognite’s solutions through Fast AI Prototyping via Cognite Atlas AI™ and Cognite Data Fusion®.
  • Agentic Solution Development: Lead the design and implementation of first-of-their-kind industrial agents. You will leverage the Atlas AI workbench to automate complex industrial workflows, ensuring solutions are technically feasible, scalable, and grounded in the contextualized data foundation of our Industrial Knowledge Graph.
  • Technical Representation & Use Case Design: Lead use case workshops and briefings, translating functional and non-functional requirements into deliverable high-level use cases. You will build executive confidence by delivering client-focused demos and reference architectures that prove the ROI of Industrial AI.
  • Product Innovation & Field Leadership: Act as the critical bridge between the field and Cognite’s Product organization. Translate “field friction” and deployment learnings into actionable product requirements and scalable best practices that strengthen Cognite’s position as the leader in Industrial DataOps.

The Impact you bring to Cognite

  • Industrial Architecture Expertise: Ability to design scalable architectures that unify siloed IT/OT/ET data into a single source of truth using Cognite Data Fusion®.
  • Agentic AI Proficiency: Demonstrated experience building with LLMs (Gemini, Claude, GPT-4) and a deep understanding of the Agentic AI landscape to create trusted, mission-critical industrial agents via Cognite Atlas AI™.
  • Full-Stack Solution Engineering: Ability to orchestrate and build end-to-end prototypes—integrating GenAI components, CDF data models, and legacy customer systems—using scalable software practices and open-source AI frameworks.
  • Technical Evangelism & Strategic Influence: The ability to distill complex technical AI solutions into compelling, value-driven narratives. You can pivot seamlessly between deep architectural sessions with engineers and strategy briefings with C-level executives.

Required Qualifications

  • 3+ years in forward deployed engineering/pre-sales engineering roles ideally in enterprise SaaS, cloud platforms, or industrial software companies.
  • Strong engineering background. Preferred fields include but are not limited to Computer Science, Mathematics, Software Engineering, Physics, and Machine Learning.
  • A strong foundation in Python and a deep understanding of the GenAI landscape (embeddings, RAG, and prompt chaining).
  • Excellent problem-solving, analytical, and communication skills.
  • Ability and interest to travel up to 50% as needed to be “on the ground” where the impact happens.

Preferred Experience

  • Education or experience in the industrial space (e.g., Petroleum, Chemical, Mechanical, or Control System Engineering).
  • Previous customer facing or sales experience.
  • You’ve worked with “messy” real-world variables from the Industries we work in (Energy, Oil and Gas, Chemicals, Life Sciences, Discrete) and understand, for example, why data quality in a refinery is harder than data quality in a web app.
  • You are excited to bridge the gap between traditional heavy industry and the future of Autonomous Operations via Cognite Data Fusion® and Agentic AI.

A snapshot of our many perks and benefits as a Cogniter

  • Competitive compensation
  • 401(k) with employer matching
  • Competitive health, dental, vision & disability coverages for employees and all dependents
  • Unlimited PTO
  • Paid Parental Leave Program
  • Employee Referral Program

Learn more about us

Equal Opportunity

Cognite is committed to creating a diverse and inclusive environment at work and is proud to be an equal opportunity employer. All qualified applicants will receive the same level of consideration for employment.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThe role requires hands-on delivery of novel industrial AI solutions while navigating complex customer environments, ambiguous data, and executive-level expectations. Frequent travel and the need to bridge product, sales, and engineering further increase its complexity.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Below market
$401
US market range (per year)$150k–$220k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The annual compensation figures are estimated for a senior US-based forward-deployed/pre-sales AI engineering role with industrial domain expertise and substantial customer travel.

Core skills

Skills and capabilities most closely associated with this opportunity.

PythonGenerative AILarge Language ModelsRetrieval-Augmented GenerationPrompt EngineeringSolution ArchitecturePre-Sales EngineeringForward Deployed EngineeringIndustrial DataOpsData IntegrationIT/OT SystemsTechnical WorkshopsExecutive PresentationsCustomer DiscoveryAI Prototyping
Sample interview questions
How would you approach building an industrial AI prototype for a customer with fragmented IT and OT data?

I would begin with a focused discovery workshop to define the business outcome, users, data sources, constraints, and success metrics. I would then identify a narrow, high-value workflow, model and contextualize the necessary data, build a secure prototype with observable evaluation criteria, and iterate with end users before planning production hardening.

Describe how you would explain a RAG-based industrial agent to both an engineer and a C-level stakeholder.

For engineers, I would explain the data pipeline, retrieval strategy, grounding controls, model selection, evaluation, and integration points. For executives, I would focus on the operational decision or workflow improved, expected ROI, risk controls, adoption path, and measurable outcomes rather than implementation details.

What challenges are common when working with industrial data, and how would you address them?

Industrial data is often incomplete, inconsistently named, time-series heavy, distributed across legacy systems, and tied to operational context. I would address this through data profiling, contextualized models, domain-expert validation, lineage and quality checks, and incremental delivery that proves value before broad expansion.

How do you balance a customer's immediate prototype request with the need for a scalable architecture?

I define a minimum viable prototype that demonstrates the requested outcome while establishing non-negotiable architectural guardrails such as secure access, reusable data models, clear interfaces, and measurable reliability. I make trade-offs explicit and create a roadmap separating fast validation work from production requirements.

Tell us about a time you turned customer feedback into a product improvement.

I would collect recurring feedback across engagements, distinguish isolated implementation issues from systemic product friction, and document the impact with concrete workflows and evidence. I would partner with product and engineering to prioritize a solution, validate it with affected users, and feed the resulting best practice back into future deployments.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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