Workhelix — Forward-Deployed Software Engineer (FDSE)
Team-facing version — internal use only. Do not share externally.
Type: Full-time | Hybrid (2–4 days in-office) | San Francisco, CA Compensation: $150K–$200K + Competitive equity Hiring count: 1 Visa sponsorship: No — not available (strict mandate) Reports to: Prateek Kukreja, Hiring Manager
About Workhelix
Workhelix unites advanced research and exhaustive data analysis to evaluate the potential of Generative AI within an organization, then generates a dynamic, interactive roadmap tailored to that organization's needs. It helps Fortune 500 companies measure and maximize AI ROI using peer-reviewed methodologies from MIT, Stanford, and Wharton. Backed by top investors and AI luminaries including Andrew Ng, Reid Hoffman, and Yann LeCun, with real revenue from Fortune 500 customers.
Founded: 2021 | Team size: 20 | Total funding: $15M Industry: AI, B2B, Data, Enterprise Website: www.workhelix.com Office: San Francisco, CA
Why Candidates Should Join
- Work with Fortune 500 clients: Solve high-impact, real-world data and engineering challenges directly with enterprise customers.
- Collaborate with world-renowned researchers: Work alongside leading AI researchers and Workhelix's data scientists, strategists, and platform engineers.
- Meaningful equity + real traction: Fast-growing AI infrastructure company with real revenue from Fortune 500 customers.
- Shape the product: Client-facing insights directly inform the product roadmap; you'll travel to client sites and work with C-suite stakeholders.
Intake Call Summary
- Hiring drivers: Anticipated contract signings plus an overwhelming volume of customer work that currently diverts product engineers. This FDE will focus solely on customer work — specifically data extraction and transformation, which customers routinely struggle with.
- Post-contract ownership: The FDE provides data schema requirements, communicates updates to the sales team, and owns data extraction and ingestion. Responsibilities include data QA, redacting sensitive information, running proprietary models to meet quality standards, and transforming data for the pipeline.
- FDE vs. FDS: The Forward Deployed Strategist (FDS) pairs closely with the FDE — leads initial meetings, ensures scope consistency, and manages stakeholder/executive communication and project management. The FDE focuses on technical depth and engineering.
- Non-negotiables: Strong technical background (Python, SQL, AWS infrastructure) plus customer-facing experience. Data engineers with customer-facing experience are considered ideal. Strong communication skills are crucial for C-suite interaction.
- Common engagement objections: Data sensitivity, security, privacy, and infrastructure.
- Seniority read (recruiter, Laura): Mid-level, ~4–7 years of experience; customer-facing experience emphasized.
- Note on seniority language: The structured requirement lists 3–5 years [Required]; the long-form role description references "at least 5 years." Treat the customer-facing requirement as the hard floor and confirm level on the screen.
The Role
As a Forward-Deployed Software Engineer at Workhelix, you'll work directly with enterprise clients to solve high-impact data and engineering challenges — embedding with client teams to understand legacy systems, extract and transform complex datasets, and build scalable solutions. This is a technical-first role: production-grade code, data pipelines, and integration with messy real-world systems.
What You'll Be Doing
- Deploy on-site or work closely with client engineering teams to extract data from enterprise systems (ERPs, data warehouses, SaaS tools)
- Write clean, efficient, maintainable code in Python, SQL, and other languages as needed
- Build and deploy data pipelines, APIs, and internal tooling to support analytics and modeling workflows
- Partner with customer stakeholders (technical and non-technical) to deeply understand their systems, workflows, and pain points
- Collaborate with Workhelix's data scientists, strategists, and platform engineers to deliver end-to-end solutions
- Surface client insights that inform the product roadmap and internal tooling priorities
Tech stack: Python, SQL, AWS
Qualifications
Seniority
- 3–5 years in a data-heavy, customer-facing software engineering role (e.g., Sales Engineer, Solutions Engineer, FDE, Customer-facing SWE) [Required]
Work Experience
- Experience in at least 1 customer-facing technical role (e.g., FDE, Solutions Architect) [Must have]
- Experience building and deploying data ingestion/integration pipelines using Python and SQL [Required]
- Deep experience with cloud infrastructure (AWS) and various enterprise systems (ERPs, warehouses) [Required]
- Experience at an early-stage startup (Seed to Series B) [Strongly preferred]
Miscellaneous
- Ability to work hybrid from the San Francisco office [Must have]
- Willingness to travel occasionally to client sites [Required]
Traits to Avoid
- Candidates who job-hop frequently (e.g., multiple roles under 1 year)
- Purely backend engineers with no interest in customer interaction
- Solutions engineers who only do demos or light integrations
Role Details
Salary$150K–$200KEquityCompetitive equityOn-site policyHybrid — 2–4 days in-office in SFVisa sponsorshipNot available (strict mandate)Employment typeFull-timeLocationSan Francisco, CA
Screening Questions
- Do you now, or will you in the future, need visa assistance to legally work in the United States?
- Can the candidate be on-site? If not, is the candidate willing to relocate?
- What is their salary expectation?
- How actively is this candidate exploring new opportunities?
Interview Process
Stage 1 — Submit candidate After submission, you'll be notified if the hiring manager wants to proceed.
Stage 2 — Recruiter Screen (20–30 min)
Stage 3 — Initial Screen with HM (30 min) Initial interview with Hiring Manager Prateek Kukreja to assess overall fit for the role and company culture.
Stage 4 — Take-Home (Offline) Take-home assignment evaluating technical skills, including scripting, Python, and SQL.
Stage 5 — Take-Home Assignment Review (1 hour) Review of the take-home, assessing communication, live analysis, and the ability to handle technical curveballs.
Stage 6 — Final Interview w/ CEO (45 min) Final interview with the CEO to ensure alignment on vision and values. Auto-approval for all future roles with this client follows.
Stage 7 — Offer Extended
Stage 8 — Candidate Hired Candidate accepts the offer and starts.
Ideal Companies & Backgrounds
Updated Apr 8, 2026
AI & Data Platforms with Forward-Deployed / Solutions Engineering Teams C3 AI, DataRobot, Domino Data Lab, Fivetran
Big Tech Customer/Partner-Facing Engineering Roles Google, Meta, Amazon Web Services (AWS), Microsoft
Data-Intensive B2B SaaS & Enterprise Software Confluent, Amplitude, Mixpanel, Braze, Retool, Samsara, Rippling, Segment, Looker
Non-Ideal Companies (avoid sourcing)
Traditional IT Consulting & Large Systems Integrators Deloitte Digital, Capgemini, Wipro, Infosys, Cognizant, Tata Consultancy Services, Booz Allen Hamilton
Companies with primarily pre-sales / demo-focused SE roles Salesforce, HubSpot, Workday, ServiceNow, Adobe, Oracle, SAP
Consumer-Focused Product Companies (lacking enterprise B2B context) Snap Inc., Pinterest, Netflix, Airbnb, DoorDash, Uber, Reddit Inc., Spotify, Tinder Note: "Show all 10 companies" was not expanded before copying — 1 company in this group is missing.
Ideal Candidate Profiles
For reference only — do not source these specific profiles.
Kevin Bai — LinkedIn Applied AI @ Anthropic | Palantir | United Nations | San Francisco, United States
- Worked at startups in a founding FDE role; helped found the FDE function at Rippling
- Has been an FDE working directly with customers
- Experience with Python and SQL
- Based in SF
Rejected Candidate Feedback
- Prioritize candidates with proven production-grade code experience building robust data pipelines using Python and SQL in enterprise settings.
- Ensure clear customer-facing exposure with direct deployment and integration experience — not just internal or demo roles.
- Screen for stability; avoid candidates with a history of frequent job-hopping to ensure long-term commitment and deeper domain expertise.
- Confirm candidates meet the onsite requirement in San Francisco with no visa needs — this is a strict mandate for the role.
Rejection-type patterns across 22 logged rejections: the most common reasons were insufficient relevant experience (HM Review stage), concerns about job stability / tenure, and visa or sponsorship not available.