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
-
1. Build or Scan
Create or review a resume.
-
2. Train Your Focus
Try a short exercise and review your practice score.
-
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
0%
Loading recorded preparation…
Open roles
1,467 jobs found · Showing 1–12
ROLE MATCH
Machine Learning Engineer, Voice Conversion at Cantina Labs
Review your resume for this role →
{"title":"Machine Learning Engineer, Voice Conversion at Cantina Labs","requirements":["Exceptional research\/development experience with large-scale audio models (\u0026gt;8B parameters, \u0026gt;500k hours of data).","Deep hands-on experience with diffusion and\/or flow-matching transformers, including practical knowledge of samplers, schedules, conditioning mechanisms, and distillation.","Strong experience with multi-node, multi-GPU distributed training (FSDP\/DeepSpeed or equivalent).","Experience with voice cloning, speech control\/steerability, or expressive speech generation."],"description":"About Cantina:\nCantina is a new social platform founded by Sean Parker with the most advanced AI character creator. Our bots are lifelike, social creatures that can interact wherever people are online\u2014across voice, video, and text. Create yourself, imagine someone new, or choose from thousands of characters to share infinitely scalable, personalized content and seamless group chat.\nIf you\u2019re excited about how AI can shape creativity and social interaction, come help us build what\u2019s next.\n\nAbout the Role:\nWe\u2019re looking for a Research \/ ML Engineer to join our Speech Team to build state-of-the-art speech systems end-to-end\u2014from data specs through production inference. You\u2019ll drive the model \u2194 data \u2194 eval flywheel for VC and adjacent tasks (controllable TTS, voice design and more), partnering closely with research, data, and infra to ship fast, reliable, and cost-aware models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.\nYou will thrive in this role if you: \n\n\nSee research and engineering as two sides of the same coin and enjoy owning work end-to-end.\n\n\nAre results-oriented, flexible, and willing to pick up whatever moves the needle.\n\n\nLike collaborating closely with infra, data, and product to ship measurable improvements.\n\n\nEnjoy designing experiments, listening tests, and metrics that correlate with user-perceived quality.\n\n\nEager to learn every-day, find and solve unique large-scale problems.\n\n\n\nWhat You\u2019ll Do:\n\n\nModel Building: Architect, implement, pre-train, fine-tune, and post-train\/alignment (e.g., GRPO\/DPO) for large-scale speech models.\n\n\nExperimental Design: Design, run, and analyze scientific experiments to advance our understanding of the models.\n\n\nTool Development: Develop and improve dev tooling to enhance team productivity.\n\n\nFull-Stack Contribution: Contribute to the entire stack, from low-level optimizations to high-level model design.\n\n\nData Ownership: Define data requirements and collaborate on acquisition, curation, augmentation, labeling quality, and synthetic data strategies.\n\n\nRigorous Evaluation: Design automated objective\/subjective evaluations\u2014listening tests, SV\/WER\/ASR-based metrics, robustness \u0026amp; bias checks, and red-team studies.\n\n\nPipeline Delivery: Harden the training \u2192 evaluation \u2192 inference pipeline; profile latency, memory, and cost; and meet production SLAs with robust monitoring and rollback.\n\n\nSafety \u0026amp; Responsibility: Contribute to safety\/consent guardrails and to misuse\/abuse mitigation for responsible speech technology.\n\n\nWhat You\u2019ll Bring:\n\n\nExceptional research\/development experience with large-scale audio models (\u0026gt;8B parameters, \u0026gt;500k hours of data).\n\n\nDeep hands-on experience with diffusion and\/or flow-matching transformers, including practical knowledge of samplers, schedules, conditioning mechanisms, and distillation.\n\n\nDeep hands-on experience training audio VAEs, neural audio codecs, and vocoders latent\/tokenizer design, reconstruction and perceptual objectives, adversarial training.\n\n\nStrong experience with multi-node, multi-GPU distributed training (FSDP\/DeepSpeed or equivalent).\n\n\nStrong software engineering skills with a proven track record of building complex systems.\n\n\nStrong with PyTorch and performance work (profiling, CUDA\/Triton\/C++ as needed) and writing reliable production-quality code.\n\n\nShipped large-scale speech\/audio or multimodal generative models to production.\n\n\nBackground in working with large-scale ML data, and the ability to iterate on data and triangulate quality using both subjective and objective signals.\n\n\nExperience with voice cloning, speech control\/steerability, or expressive speech generation.\n\n\nNotable publications and\/or open-source contributions in speech\/audio\/ML.\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
- Exceptional research/development experience with large-scale audio models (>8B parameters, >500k hours of data).
- Deep hands-on experience with diffusion and/or flow-matching transformers, including practical knowledge of samplers, schedules, conditioning mechanisms, and distillation.
- Strong experience with multi-node, multi-GPU distributed training (FSDP/DeepSpeed or equivalent).
- Experience with voice cloning, speech control/steerability, or expressive speech generation.
Application board
Sign in to track applications privately.