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role
togetherai / Forward Deployed Engineer (Inference & Post-Training)
model
anthropic/claude-sonnet-4.6
created
2026-10-07T03:53

Interviewer

Rochelle Mattern is the Head of Field Engineering at Together AI, a role she stepped into in September 2025 after a nearly two-year tenure at SambaNova Systems where she led Worldwide Field Engineering and Solutions Engineering. Her career arc runs from hardware/electrical engineering at Cisco and United Technologies through Google Cloud customer engineering (President's Club winner) to leading technical pre- and post-sales organizations at AI-native companies. As the hiring leader for this FDE role, she will likely probe customer-facing technical depth, ability to drive time-to-value, and the product feedback loop instinct — all themes she has personally championed at SambaNova and Forethought. Shared context with the candidate includes deep familiarity with open-source LLM ecosystems, developer-facing platform thinking, and a track record of building 0-to-1 technical functions.

Questions to ask them (20)

categoryquestionwhy
interviewer_experience You built the field engineering function at SambaNova from Director to Head of WW, and now you're doing something similar at Together AI. What was the biggest structural lesson from SambaNova that you're deliberately applying — or deliberately not repeating — as you build out this team? Surfaces her mental model for what 'good' field engineering looks like at an AI infrastructure company, and signals you've studied her trajectory rather than just her current title.
interviewer_experience At Forethought you reduced time-to-value by 75% through revamped onboarding and trial frameworks — that's a striking number. How much of that playbook is transferable to Together AI's inference and post-training context, where the technical surface area is so much deeper? Shows you read her profile carefully and invites her to articulate what 'opinionated onboarding' means in practice at Together AI — directly relevant to a core FDE responsibility.
interviewer_experience You came up through Google Cloud customer engineering supporting some of their largest strategic accounts before moving into AI-native companies. How has your definition of 'technical win' evolved as the products shifted from cloud infrastructure to GenAI platforms? Builds rapport around her Google tenure and gets her talking about how she evaluates FDE success — useful for calibrating what she'll measure you against.
role_team_dynamics The JD is explicit that FDE is not a replacement for a Solutions Architect — you're a deep-domain specialist partnering with SAs. In practice, where does that boundary get tested most, and how does the team navigate it when a customer engagement requires both breadths? This is the most operationally ambiguous part of the role. Understanding the real division of labor prevents stepping on SA toes and clarifies where you'll spend your time.
role_team_dynamics What does success look like at 30, 60, and 90 days for this role — and is the expectation that I'm already running independent customer engagements by day 90, or is there a ramp period where I'm shadowing existing accounts first? Practical signal on ramp expectations and whether there's a structured onboarding or a 'drink from the firehose' culture.
role_team_dynamics How many strategic accounts would a single FDE typically be aligned to at any given time, and what's the mechanism for deciding when an account graduates from needing FDE-level support to being handled by the SA or CSM layer? Capacity and account management clarity — critical for understanding whether the role is sustainable and how impact is measured.
role_team_dynamics The JD mentions FDEs contribute back to the product where needed. Can you give me a concrete example of a field insight that actually changed something on the inference or post-training roadmap in the last six months? Tests whether the product feedback loop is real or aspirational, and signals you care about leverage beyond individual customer wins.
technical_environment Together AI serves 400+ trillion tokens a month across a marketplace of open models. When an FDE is tuning KV cache or selecting tensor parallelism configs for a strategic customer, are they working directly in Together's inference infrastructure, or are they building configurations that get handed off to a platform engineering team to deploy? Clarifies the actual technical access and ownership model — the difference between advising and doing is significant for someone with hands-on inference engine depth.
technical_environment For post-training engagements — LoRA, DPO, GRPO pipelines — is the FDE running training jobs on Together's compute, helping customers run on their own infrastructure, or both? And how mature is the tooling around experiment tracking and reproducibility for customer fine-tuning runs today? Directly relevant to the candidate's RL workbench and post-training background; surfaces whether there's greenfield tooling work to be done or a mature platform to operate within.
technical_environment Which inference engines are most prevalent in your current strategic customer deployments — vLLM, TensorRT-LLM, SGLang — and is there an internal standard, or does the FDE genuinely need to be fluent across all three depending on the customer's hardware profile? Practical depth question that signals you know the landscape and want to understand where to invest preparation time before day one.
culture_working_style How does the field engineering team handle a situation where an FDE's recommendation to a customer conflicts with the current platform's capabilities — do you lean toward honest scoping of limitations, or is there pressure to find a workaround to protect the deal? Tests for integrity and customer-first culture versus sales pressure dynamics — important for a role that sits at the intersection of CX, Engineering, and Sales.
culture_working_style Together AI is clearly moving fast — 400T tokens a month is a significant operational scale. How does the team balance the urgency of supporting strategic POCs with the discipline needed to document learnings and avoid reinventing solutions across accounts? Surfaces knowledge management culture and whether there's institutional memory or every FDE is starting from scratch on each engagement.
culture_working_style When an FDE disagrees with a product or engineering decision that's affecting customer outcomes — say, a configuration limitation that's costing a strategic account performance — what's the actual escalation path, and how receptive is the engineering org to field-driven urgency? Tests psychological safety and cross-functional influence — critical for someone whose value proposition is the product feedback loop.
growth_development Your own path went from individual contributor customer engineer at Google to building and leading global field engineering organizations. For someone joining as an FDE today, is there a defined path toward technical leadership or team-building, or is the expectation that this role stays deeply IC for the foreseeable future? Anchors the question in her own career trajectory, making it feel like genuine curiosity rather than impatience, while gathering real signal on growth ceiling.
growth_development The inference and post-training space is evolving extremely fast — new architectures, new training paradigms, new engines every few months. How does Together AI invest in keeping FDEs at the frontier technically, beyond what they pick up from customer engagements? Signals you're thinking about long-term technical currency, not just current competency.
strategy_vision Together AI positions itself as the 'AI Native Cloud' with a marketplace of open models. As customers increasingly want to own and adapt models rather than just call APIs, how does the FDE role evolve — are you seeing the center of gravity shift more toward post-training and fine-tuning engagements relative to pure inference optimization? Strategic question that shows you've internalized the company's positioning and want to understand where the field engineering function is heading.
strategy_vision Together's customer list includes Cursor, Decagon, ElevenLabs — these are AI-native builders, not traditional enterprises. Does the FDE motion look meaningfully different for AI-native customers versus more traditional enterprise accounts, and is the team investing in both segments equally? Surfaces customer segmentation strategy and whether the role will require context-switching between very different buyer profiles.
strategy_vision With GRPO and reasoning model post-training becoming a major focus across the industry, how is Together AI positioning its platform for customers who want to replicate or build on top of reasoning-style training pipelines — and is that an area where FDEs are expected to be at the frontier? Directly relevant to the candidate's RL workbench work and signals deep awareness of the current post-training landscape.
shared_context I've been building out a post-training RL workbench that benchmarks GRPO, DPO, and PPO across TRL, VeRL, OpenRLHF, and NeMo RL — one of the things I kept running into was how much framework choice affects throughput and memory footprint on the same hardware. Is framework-agnostic benchmarking something Together AI's customers are actively asking for, or do most strategic accounts arrive with a strong framework preference already? Builds direct rapport by connecting the candidate's hands-on RL workbench project to a real customer question, demonstrating both technical depth and field-readiness.
shared_context My background includes a lot of developer platform work — SDKs, DevPortals, onboarding frameworks — and I've noticed that the hardest part of inference optimization for customers is often not the tuning itself but getting them to instrument and surface the right metrics to know what to tune. Is that a gap you're seeing in the field, and is there appetite for FDEs to contribute tooling or documentation that closes it systematically? Connects the candidate's platform/developer-tooling background to a real field engineering pain point, positioning their PM and SDK experience as additive rather than tangential.

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