Founding ML Platform Engineer

Talantir; United States; Remote; USD 90,000 - 200,000; Full time

Job summary

Deep Reasoning Labs is building a deep reasoning layer for LLMs, focusing on scalable, reproducible training and evaluation. You will own the internal MLOps platform and the research inference layer, ensuring fast, auditable iterations from training to rollout.

This role emphasizes systems engineering, multi-GPU training, and cost-efficient GPU orchestration. Remote-first with US/Canada overlap and occasional in-person sprints in SF preferred.

Qualifications

• Strong systems + ML infra experience for end-to-end training/evaluation pipelines.
• Experience running LLM inference serving with batching and caching.
• Proficiency with multi-GPU training and cost-aware orchestration.

Responsibilities

• Own training + research MLOps platform and inference layer.
• Build cost-efficient, reproducible pipelines (SFT, verifier/PRM, RLVR).
• Manage data pipelines, dataset versioning, model registry, and experiment tracking.

Skills

Systems engineering

ML infra

PyTorch distributed

Cloud/IaC

Experiment tracking

Cost optimization

Profiling

Tools

Kubernetes

AWS/GCP

Terraform/CloudFormation

vLLM/SGLang

DeepSpeed

Job description

Company

Deep Reasoning Labs is building a deep reasoning layer for LLMs focused on long-horizon coding. Our iteration loop depends on a fast, reproducible training + evaluation platform: SFT, verifier/PRM training, and RLVR-style post-training, with strong lineage and cost-efficient GPU execution.

Role

You will own the internal training + research MLOps platform: scalable PEFT post-training (LoRA/QLoRA), dataset/label pipelines, data acquisition + ingestion, evaluation automation, experiment tracking, and cost-efficient GPU orchestration (including spot/preemptible strategies). You will also own the research inference layer (model serving, batching/caching, version routing) that closes the loop between training, rollouts, and evaluation. This is a "systems + research acceleration" role: your output is that research iterations become reliable, fast, price-efficient, and auditable.

What you'll work on

Training + post-training pipelines (PEFT-first):

• Reproducible pipelines for: SFT, verifier/PRM training, and RLVR-style post-training
• Build repeatable LoRA/QLoRA fine-tuning pipelines (SFT + verifier/PRM training + RLVR-style updates where used), optimized for cost and iteration speed
• Robust checkpointing/resume and failure handling for long-running jobs
• Artifact management: dataset versions, configs, checkpoints, eval results, and model registry with lineage

Inference serving + rollout collection (research-grade):

• Operate an LLM serving stack (e.g., vLLM/SGLang) for policy + verifier/PRM models
• Optimize throughput/cost via batching, caching, scheduling, and profiling
• Build reliable rollout collection and replay tooling (configs, model versions, artifacts, traces)

GPU orchestration + cost efficiency:

• Multi-GPU training reliability (single-node initially; scale up over time)
• Spot/preemptible strategy: interruption-tolerant training, autoscaling, queueing, capacity-aware scheduling
• Performance tuning: profiling, dataloading, communication overhead reduction, utilization improvements

Data acquisition + ingestion (training/eval):

• Build ingestion pipelines for code/text/trace datasets, including programmatic collection from select web sources where appropriate
• Implement deduping, normalization, provenance tracking, and dataset versioning
• Ensure operational robustness (rate limiting, retries, incremental crawls, change detection) and practical compliance hygiene (respect access policies/ToS where required)

What success looks like (first ~90 days)

• One-command reproducible pipeline for baseline SFT + verifier/PRM training + evaluation
• Spot/preemptible training that is interruption-tolerant (checkpoint/resume) and not babysat
• Clear dataset + model lineage (you can answer: "what data created this model and what changed?")
• Automated eval + regression detection integrated into the iteration loop

Requirements (must-have)

• Strong systems + ML infra experience: training pipelines, data systems, reliability engineering
• Strong data engineering fundamentals: building ingestion pipelines, handling messy sources, deduping, and dataset versioning/provenance.
• Experience running LLM inference serving (vLLM/SGLang/TGI), including batching/caching and performance tuning.
• Hands-on experience running multi-GPU training (PyTorch distributed: DDP/FSDP/DeepSpeed/etc.)
• Strong cloud + IaC skills (AWS/GCP; Terraform/CloudFormation/Pulumi)
• Track record building reproducible pipelines (artifact/version management, experiment tracking)
• Performance mindset: profiling, bottleneck identification, cost/perf tradeoffs

Nice-to-have

• Spot/preemptible fleet orchestration at scale (autoscaling, capacity strategy)
• RLHF/RLAIF infrastructure (reward models, preference pipelines, rollout collection)
• LLM serving/inference performance experience (to close the train serve loop)
• Experience building reliable crawlers/scrapers and incremental ingestion systems (queueing, rate limits, backoff, change detection).
• Familiarity with code datasets, build/test tooling, or program analysis signals

Tech stack (likely)

• Linux, Python, PyTorch distributed (DDP/FSDP/DeepSpeed), job orchestration (Kubernetes/ECS/queues), object storage, experiment tracking, IaC, internal eval infrastructure.

Location / work model

Remote-first (US/Canada). Strong preference for overlap with Pacific Time. Periodic in-person sprints in SF are a plus.

Compensation

Market-competitive base (location-based) + meaningful founding-level equity.

Pay: $90,000.00 - $200,000.00 per year

Work Location: Remote