Forward Deployed Engineer

About the job

This role is being recruited by CoffeeSpace on behalf of a well-funded AI infrastructure startup building a data curation platform for model training.

We’re identifying a small number of experienced customer-facing technical ICs from our network.

If there’s a strong fit, we’ll introduce you directly to the founding / leadership team.

Job Title: Forward Deployed Engineer

Location: Redwood City, CA, hybrid / 4 days in-office

Compensation: $230k–$300k base + competitive equity

Employment type: Full-time

Visa: Sponsorship, transfers, and relocation support available

Travel: Up to ~15% customer-site travel

About the company

This company is building data curation infrastructure for AI model training.

The core idea is simple: models are what they eat. A large portion of model training compute is wasted on data that is already learned, irrelevant, low-quality, or even harmful. This company helps AI teams automatically curate and optimize massive training datasets so they can train better models with less wasted compute.

Its platform has shown the ability to meaningfully reduce training time and cost, improve model performance, and help smaller models outperform larger ones while using less inference compute.

The company is well-funded, backed by leading AI and infrastructure investors, and already working on one of the most important bottlenecks in modern AI: training data quality.

The team is small but growing quickly, with an in-person engineering culture based in Redwood City.

About the role

The company is hiring a Forward Deployed Engineer to own the technical success of its most strategic customers.

This is a post-sales, customer-facing IC role for someone who is deeply technical, hands-on, and comfortable operating across enterprise infrastructure, distributed systems, cloud environments, data pipelines, Kubernetes, and ML workflows.

You will work directly with customers to onboard, deploy, and scale the company’s platform across complex environments, including AWS, GCP, Azure, on-prem infrastructure, and hybrid Kubernetes setups.

You should be the kind of person who can sit with a customer executive team to understand what matters, then go deep into architecture, infrastructure configs, debugging, and implementation details with their engineering team.

Why this role is remarkable

• You’ll work on one of the most important bottlenecks in AI: training data quality
• The company is solving a real infrastructure problem around wasted training compute, data quality, and model performance
• You’ll own strategic customer deployments rather than only advising from the sidelines
• You’ll work across AI, distributed systems, cloud infrastructure, data pipelines, and enterprise deployment
• You’ll help shape post-sales playbooks from 0 to 1 at a fast-growing technical startup
• You’ll stay deeply hands-on while working close to customers and the product roadmap
• Compensation is strong: $230k–$300k base plus competitive equity
• Visa sponsorship, transfers, and relocation support are available

What you’ll do

• Own end-to-end onboarding, deployment, and production rollout for strategic enterprise accounts
• Serve as the primary technical advisor and trusted point of contact for customers
• Design secure, scalable workflows across compute, storage, networking, IAM, Kubernetes, and distributed systems
• Deploy software into complex customer environments, including cloud, on-prem, and hybrid infrastructure
• Translate ambiguous customer requirements into concrete architecture and implementation plans
• Partner with Sales, Engineering, and Research to shape technical strategy and improve the product
• Build repeatable playbooks and processes for post-sales deployment from 0 to 1
• Travel to customer sites when needed for critical deployments and high-stakes technical engagements

You might be a fit if you have

• 5+ years of experience in a post-sales technical IC role, such as Forward Deployed Engineer, Solutions Engineer, Solutions Architect, Implementation Engineer, Customer Engineer, or similar
• Hands-on experience deploying software into enterprise customer infrastructure
• Strong experience with distributed systems, data infrastructure, backend systems, or ML infrastructure
• Strong proficiency with Python and AWS
• Exposure to GCP, Azure, on-prem infrastructure, Kubernetes, or infrastructure-as-code
• Experience with data pipelines, model training workflows, model deployment, or broader ML/AI systems
• The ability to debug technical issues end-to-end and communicate clearly with both customers and internal engineering teams
• Comfort working in a startup or high-ambiguity environment

Interested in relocating to United State? Check out our comprehensive Relocation Jobs in United State page with detailed relocation packages and benefits.

• A technical degree, ideally in CS or a related field

This is probably not the right fit if you are

• Purely pre-sales with no post-sales implementation ownership
• A pure software engineer with no customer-facing technical experience
• Mostly in leadership now and no longer hands-on as an IC
• More customer success than technical implementation
• Primarily from a heavy consultancy background without deep product deployment ownership
• Someone with multiple short tenures under one year

Next steps

1. Apply via this LinkedIn job post.
2. We’ll review and reach out if there’s a strong match.
3. If aligned, we’ll introduce you directly to the hiring team.
4. If this role is not the right fit, we may suggest and make introductions to other high-signal startup roles we’re actively recruiting for, always with your permission.

A quick note on authenticity

This is a real, active role CoffeeSpace is recruiting for in close partnership with the hiring team. We do not post speculative roles and work directly with teams on their actual hiring needs.