CAREERSFORGE / YOUR NEXT MOVE
Ignis Your Career Coach & Workspace
See what you’ve practiced, what your resume demonstrates, and what to work on next.
Your next career move starts here
Your free AI career coach. Choose a goal, work through your next step, and return to review your progress.
Try it as a guest. Save Progress creates an account for your recorded activities and conversation history.
Readiness · Getting started
Activities recorded · 0
PRIVATE, IN-BROWSER TOOL
Decode a Rejection Email
Paste only what you’re comfortable reviewing. This tool matches common wording in your browser; it does not save the email, send it to AI, or know why the employer made its decision.
Wording found in the email
Possible meaning — not a fact about this decision
Three practical next steps
60-SECOND CLARITY ROUND
Spot the Red Flag
These are illustrative practice snippets, not quotes from real employers. A phrase alone does not prove a workplace is harmful; use it as a prompt to ask for specifics.
Choose the wording that needs a follow-up question. Clear scope, documented onboarding, and reasonable boundaries are useful context too.
Use the explanation as a conversation starter, not as a verdict on a company.
Quick Start: How Ignis Works
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1. Build or Scan
Create or review a resume.
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2. Train Your Focus
Try a short exercise and review your practice score.
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3. Consult Ignis
Use your recorded results to plan a next step.
Your next step with Ignis
Ask Ignis to connect your recorded results and suggest one practical next step.
Your next step starts here
Your performance radar unlocks after your first recorded activity. Complete a 2-minute mock interview or scan a resume to generate your report.
Career Health Radar
Complete a resume review or a Brain Training session to see your results here.
Readiness labels summarize recorded practice. They are planning cues, not hiring predictions or a measure of job suitability.
Strengths and priorities
Complete a resume review to see strengths and improvement priorities.
Open Interview Practice →Top matched roles
Compare your evidence with a role in Job Radar to calculate a match.
Activity mix
Your next step starts here
Your practice dashboard unlocks after your first recorded activity. Begin with a short interview or resume review, then explore your daily sprint and training.
Daily Career Sprint
Daily Confidence Pill
Loading today’s practical career prompt…
Optional: breathe in for 4 seconds, then out for 4.
TODAY’S CHALLENGE
One small practice, chosen for today
Build a little momentum with a short reasoning exercise or a job-description clarity round.
Brain Training
No synced game results yet.
Skill scores average the latest result from each played exercise in that category. Open a category to see each exercise.
YOUR ROLE, YOUR NEXT MOVE
Job Radar
Explore published opportunities, filter by your priorities, and open a listing to review its requirements and apply. Choose “Open match details in Workspace” to compare your analyzed resume with the role and plan your next step.
Tool preparation
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Open roles
1,467 jobs found · Showing 1–12
ROLE MATCH
Machine Learning Engineer, Ops at Cantina Labs
Review your resume for this role →
{"title":"Machine Learning Engineer, Ops at Cantina Labs","requirements":["Production experience with Kubernetes, CI\/CD and cloud infrastructure.","Experience with speech or audio models (TTS, ASR, voice conversion).","Experience with inference frameworks such as vLLM (vLLM-Omni), SGLang, TensorRT-LLM, TensorRT or Triton Inference Server."],"description":"About Cantina:\nCantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning.\nIf you\u0027re excited about the potential AI has to shape human creativity and social interactions, join us in building the future!\n\nAbout the Role:\nWe are looking for an ML Engineer to own the production inference stack for our text-to-speech (TTS) and speech recognition (ASR) models, end to end. Our research team delivers trained model checkpoints; you will productionize them as low-latency, real-time streaming services that run cost-efficiently on GPUs. The role covers the inference engines, the Kubernetes infrastructure they run on, and the performance benchmarking and accuracy validation that let us ship changes safely. You will bridge research and production, and own latency, throughput, cost and accuracy as we scale.\n\nWhat You\u2019ll Do:\n\n\nBuild and optimize streaming inference engines for TTS and ASR models, including multi-stage model pipelines.\n\n\nOptimize latency (time to first chunk, p50\/p99) and throughput using continuous batching, CUDA Graphs, torch.compile and mixed precision.\n\n\nMaintain numerical parity with the reference implementation through automated accuracy regression tests, per stage and end to end.\n\n\nDeploy and operate GPU inference services on Kubernetes: autoscaling, containerization and infrastructure as code.\n\n\nBuild CI\/CD for model artifacts, with model versioning and reproducible releases.\n\n\nOwn observability and performance benchmarking: p50\/p99 latency, real-time factor (RTF), GPU utilization and cost per request.\n\n\nPartner with research to productionize new models.\n\n\n\nWhat You\u2019ll Bring:\n\n\nHands-on experience deploying, scaling and optimizing ML models in production, beyond consuming model APIs.\n\n\nWorking knowledge of the full inference stack, from CUDA kernels to serving frameworks, with depth in some layers; expertise across all of them is not expected.\n\n\nGPU performance optimization: profiling (Nsight Systems, PyTorch Profiler), memory bandwidth, CPU-GPU synchronization.\n\n\nSolid understanding of model serving: continuous batching, KV cache management, request scheduling.\n\n\nProduction experience with Kubernetes, CI\/CD and cloud infrastructure.\n\n\nStrong Python and PyTorch, and a rigorous approach to performance benchmarking.\n\n\nExperience with speech or audio models (TTS, ASR, voice conversion).\n\n\nExperience with inference frameworks such as vLLM (vLLM-Omni), SGLang, TensorRT-LLM, TensorRT or Triton Inference Server.\n\n\n\nCompensation:\nWhen determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.\n\nBenefits for U.S.-based roles:\n\n\nCompetitive salary and generous company equity\n\n\nMedical, dental, and vision insurance \u2013 99.99% of premiums covered by Cantina\n\n\n42 days of paid time off, including:\n\n\n15 PTO days\n\n\n10 sick days\n\n\n15 company holidays\n\n\n2 floating holidays\n\n\n\n\nGenerous parental leave \u0026amp; fertility support\n\n\n401(k) retirement savings plan\n\n\nLifestyle spending account \u2013 $500\/month to use however you\u2019d like\n\n\nComplimentary lunch and snacks for in-office employees\n\n\nOne Medical membership, and more!"}0%
Resume evidence match
Analyze your resume to calculate your match.
The percentage summarizes matching terms in the resume and role criteria. Review the requirements, experience level, qualifications, and application instructions before applying.
Analyze my resume →Signals found in your resume
Signals to review or strengthen
Review the listing criteria
- Production experience with Kubernetes, CI/CD and cloud infrastructure.
- Experience with speech or audio models (TTS, ASR, voice conversion).
- Experience with inference frameworks such as vLLM (vLLM-Omni), SGLang, TensorRT-LLM, TensorRT or Triton Inference Server.
Application board
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