For engineers within three years of graduation. AI residencies, new-grad ML and applied AI roles, summer internships. A lower starting comp band -- but the steepest trajectory in the industry. And an OpenTalent screening calibrated for early-career, so you're not penalized for not yet having a five-year shipping history.
Early-career members have moved into programs at
// Three early-career paths
Early-career hiring at frontier labs and AI-native companies isn't one job market -- it's three. Each has a different bar, a different program shape, and a different next step.
Time-bounded research- or engineering-track programs at frontier labs. Heavy mentorship, structured project work, often a convert-to-full-time decision point at the end.
Recent grads (MS or PhD common, BS possible) wanting frontier-lab research depth. Best path into research orgs.
Direct-to-full-time hiring as a member of technical staff or new-grad engineer. ML engineering and applied AI specializations especially. Faster path, fewer training wheels.
New grads (BS, MS) with shipped open-source or strong project portfolio. Best path into ML engineering and applied AI.
Summer programs at frontier labs and AI-native companies. Owned project, mentor, end-of-summer presentation. Strong conversion rate into return offers and residencies.
Current undergrads and PhDs. The lowest-friction way to learn whether frontier AI is the work you want.
// Tracked programs -- 2026 cycle
A representative slice of the early-career programs OpenTalent's network is actively placing into this cycle. Application windows, conversion rates, and program shape vary; we keep this list updated as labs change cadence.
One-year program for researchers and engineers early in their careers. Mentored project work in post-training, alignment, or interpretability. Convert-to-full-time decision at month 9.
Six-month engineering- and research-track residency. Embedded in product or research teams; emphasis on shipped artifacts. Strong conversion rate to full-time.
One-year research-engineering programs at DeepMind. Pre-doc for early-career; AI residency for new-grad research engineers. Mentored work on Gemini-adjacent teams.
Full-time technical-staff role for new-grad engineers shipping product or research-engineering work. No formal "training wheels" period -- you're a real IC from day one.
Direct-to-full-time hiring for new-grad engineers on product and infra teams. Internship-to-return is the most common path into this role.
Research-track scholars program with a focus on applied research at the frontier. Multi-month, remote-friendly, structured mentorship from senior researchers.
Six-month research internship for current Masters and PhD students. Direct work with the post-training team on open-weights model release pipelines.
Full-time roles for new-grad engineers building India's frontier AI products. Strong path for engineers from Indian universities; the most active early-career frontier hire in India.
// What programs grade on
Early-career rubric is different. Senior IC panels grade shipped work; early-career panels grade potential-- taste in problem selection, learning velocity, and the seriousness of the work you've put together so far.
You don't need a five-year shipping history. You need one serious project -- an open-source eval harness, a reproduced paper, a deployed agent, a meaningful internship deliverable -- that you can talk about end-to-end.
"Tell me about something you learned in the last six months and how you'd teach it" -- almost every early-career panel asks this. They're grading how fast you go from new topic to confident take, not whether you knew it before.
Of all the projects you could have built last summer, why this one? Strong early-career candidates have a clear answer that's about the problem, not about the resume.
Can you read and modify an unfamiliar codebase -- vLLM, TRL, a Hugging Face training script -- and find a subtle bug? Take-home loops test this directly. The signal is how you debugged it, not whether you got the right answer.
Senior engineers and PMs need to be able to work with you from day one. Panels grade whether you can explain your project to someone who isn't an expert in your sub-area in 5 minutes without losing them.
Programs aren't classrooms. The work is open-ended. Panels grade how you talk about a time you scoped your own project -- and what you'd have done differently.
// The accelerated screening
The standard OpenTalent five-stage screening is calibrated against senior engineers. For early-career applicants we run a four-stage path that grades potential instead -- same bar on rigor, different evidence.
Stage 3 (the standard 13-day real-world project) is replaced with a focused 4-5 day exercise calibrated to your specialization. Stage 5 (continued excellence) is replaced with structured mentorship and check-ins through your first program.
The bar on Stages 1, 2, and 4 -- language & reasoning, deep skill review, and live panel -- stays the same. Less than 5% of early-career applicants make it through, in line with our overall rate.
Network membership is identical: you get full access to AI Job Match, Resume AI, Cohire Copilot, Cohire, and Application Autofill -- and the same quiet hiring channel into frontier-lab programs.
Structured interview -- communication, ML literacy, structured thinking. Same as senior screen.
Domain assessment -- calibrated to your specialization (research, ML eng, applied).
4-5 day applied project. Calibrated to early-career scope -- strong potential, not five-year history.
30-minute panel with senior practitioners. Probes depth, judgment, creativity under pressure.
Monthly check-ins through your first program. The membership bar continues -- same as senior network.
// Compensation benchmarks
Total compensation (base + equity + bonus, annualized; or monthly stipend for internships). Sourced from network-verified early-career offers in the past 12 months.
For new-grads with BS/MS/PhD. PhD and high-leverage specializations land at the upper end of each band.
| Path | Range | Median | Bg. |
|---|---|---|---|
| Frontier-lab AI residency | $220K -- $420K | $310K | MS / PhD |
| Frontier-lab new-grad MTS | $320K -- $520K | $420K | BS / MS / PhD |
| AI-native startup new-grad eng. | $240K -- $400K | $310K | BS / MS |
| Frontier-adjacent product new-grad | $280K -- $460K | $360K | BS / MS |
| Frontier-lab summer intern | $15K -- $25K/mo | $20K/mo | UG / PhD |
| India early-career (Sarvam, Krutrim) | ₹35L -- ₹85L | ₹52L | BS / MS |
// The OpenTalent prep path
The same four-step prep we recommend for senior engineers -- calibrated for early-career. Open for any current student or new grad in the network.
// By the numbers
Early-career members in the OpenTalent network -- current students and new grads.
Frontier-lab residency and new-grad programs actively tracked by the network.
Acceptance rate of early-career applicants into the network. The bar is real.
Of early-career members placed into a program get an offer within 90 days.
I was a final-year undergrad with one open-source eval harness and a year of part-time research. OpenTalent's early-career screening didn't punish me for not having a five-year shipping history -- it just calibrated. I had three residency offers in my hand a month after graduation.
AI residency new-grad -- joined a frontier lab Q1 2026
// FAQ
Yes -- if you're targeting a summer internship or a fall residency cycle. We accept current students into the early-career track and the screening accounts for where you are. Membership gives you AI Job Match for internships, Cohire for trajectory planning, and Cohire to run application loops while you're studying.
If you're a first- or second-year undergrad without significant projects yet, focus on building one serious thing first. The screening grades evidence, not enthusiasm.
Not always. PhD candidates are well-represented in residency cohorts (about 55% of placements), but undergrads and Masters-level engineers regularly get in too -- typically those with a published preprint, a serious open-source contribution, or a demonstrated track record on a sub-area like RLHF, agents, or evals.
Cohire Copilot will tell you honestly whether your background is realistic for a given residency cycle, and which gaps to close.
The acceptance rate is similar (~4.6% vs. ~3% overall) -- so the answer is "calibrated, not lower." We grade potential and evidence instead of five-year shipping history, but Stage 1, Stage 2, and Stage 4 of the screening are unchanged.
Joining the network is hard. Staying in it as you grow is what continued excellence is for.
School isn't load-bearing in our screening. Frontier labs absolutely have school preferences, but our screening grades the work -- the residency programs we feed into see your project portfolio, not your transcript header. Plenty of our placements come from non-target schools.
Depends on where you want to land. Residencies are the strongest path into research orgs at frontier labs. Direct new-grad roles are the strongest path into ML engineering and applied AI.
Cohire Copilot will model both paths for your specific profile and tell you which has the higher likelihood of landing where you want to be in 18 months.
Free for OpenTalent network members. The hiring lab pays the placement fee -- never you. To join the network, apply through the early-career path.
// Where the early-career path leads
Most network members start on the early-career track and grow into one of the three senior-IC tracks within three years. Open the three to see where each one heads.
Apply to OpenTalent's early-career track. Free, accelerated screening calibrated for new grads. ~4.6% acceptance rate -- the ones who make it see programs the broader market doesn't.